1
|
Desvars-Larrive A, Vogl AE, Puspitarani GA, Yang L, Joachim A, Käsbohrer A. A One Health framework for exploring zoonotic interactions demonstrated through a case study. Nat Commun 2024; 15:5650. [PMID: 39009576 PMCID: PMC11250852 DOI: 10.1038/s41467-024-49967-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/24/2024] [Indexed: 07/17/2024] Open
Abstract
The eco-epidemiology of zoonoses is often oversimplified to host-pathogen interactions while findings derived from global datasets are rarely directly transferable to smaller-scale contexts. Through a systematic literature search, we compiled a dataset of naturally occurring zoonotic interactions in Austria, spanning 1975-2022. We introduce the concept of zoonotic web to describe the complex relationships between zoonotic agents, their hosts, vectors, food, and environmental sources. The zoonotic web was explored through network analysis. After controlling for research effort, we demonstrate that, within the projected unipartite source-source network of zoonotic agent sharing, the most influential zoonotic sources are human, cattle, chicken, and some meat products. Analysis of the One Health 3-cliques (triangular sets of nodes representing human, animal, and environment) confirms the increased probability of zoonotic spillover at human-cattle and human-food interfaces. We characterise six communities of zoonotic agent sharing, which assembly patterns are likely driven by highly connected infectious agents in the zoonotic web, proximity to human, and anthropogenic activities. Additionally, we report a frequency of emerging zoonotic diseases in Austria of one every six years. Here, we present a flexible network-based approach that offers insights into zoonotic transmission chains, facilitating the development of locally-relevant One Health strategies against zoonoses.
Collapse
Affiliation(s)
- Amélie Desvars-Larrive
- Centre for Food Science and Veterinary Public Health, Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria.
- Complexity Science Hub, Vienna, Austria.
| | - Anna Elisabeth Vogl
- Centre for Food Science and Veterinary Public Health, Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Gavrila Amadea Puspitarani
- Centre for Food Science and Veterinary Public Health, Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria
- Complexity Science Hub, Vienna, Austria
| | | | - Anja Joachim
- Centre of Pathobiology, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Annemarie Käsbohrer
- Centre for Food Science and Veterinary Public Health, Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria
| |
Collapse
|
2
|
Kim E, Kim Y, Jin H, Lee Y, Lee H, Lee S. The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data. Front Public Health 2024; 12:1386495. [PMID: 38827618 PMCID: PMC11140122 DOI: 10.3389/fpubh.2024.1386495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.
Collapse
Affiliation(s)
- Eunmi Kim
- Institute of Mathematical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Yunhwan Kim
- College of General Education, Kookmin University, Seoul, Republic of Korea
| | - Hyeonseong Jin
- Department of Mathematics, Jeju National University, Jeju, Republic of Korea
| | - Yeonju Lee
- Division of Applied Mathematical Sciences, Korea University—Sejong, Sejong, Republic of Korea
| | - Hyosun Lee
- Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Sunmi Lee
- Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| |
Collapse
|
3
|
Brooks SJ, Jones VO, Wang H, Deng C, Golding SGH, Lim J, Gao J, Daoutidis P, Stamoulis C. Community detection in the human connectome: Method types, differences and their impact on inference. Hum Brain Mapp 2024; 45:e26669. [PMID: 38553865 PMCID: PMC10980844 DOI: 10.1002/hbm.26669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.
Collapse
Affiliation(s)
- Skylar J. Brooks
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- University of California BerkeleyHelen Wills Neuroscience InstituteBerkeleyCaliforniaUSA
| | - Victoria O. Jones
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Haotian Wang
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Chengyuan Deng
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | | | - Jethro Lim
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
| | - Jie Gao
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Prodromos Daoutidis
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Catherine Stamoulis
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- Harvard Medical SchoolDepartment of PediatricsBostonMassachusettsUSA
| |
Collapse
|
4
|
Ran Y, Xu XK, Jia T. The maximum capability of a topological feature in link prediction. PNAS NEXUS 2024; 3:pgae113. [PMID: 38528954 PMCID: PMC10962729 DOI: 10.1093/pnasnexus/pgae113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/21/2024] [Indexed: 03/27/2024]
Abstract
Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature's capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.
Collapse
Affiliation(s)
- Yijun Ran
- College of Computer and Information Science, Southwest University, Chongqing 400715, P.R. China
- Center for Computational Communication Research, Beijing Normal University, Zhuhai 519087, P.R. China
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, P.R. China
| | - Xiao-Ke Xu
- Center for Computational Communication Research, Beijing Normal University, Zhuhai 519087, P.R. China
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, P.R. China
| | - Tao Jia
- College of Computer and Information Science, Southwest University, Chongqing 400715, P.R. China
| |
Collapse
|
5
|
Moutinho JP, Magano D, Coutinho B. On the complexity of quantum link prediction in complex networks. Sci Rep 2024; 14:1026. [PMID: 38200071 PMCID: PMC10781705 DOI: 10.1038/s41598-023-49906-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024] Open
Abstract
Link prediction methods use patterns in known network data to infer which connections may be missing. Previous work has shown that continuous-time quantum walks can be used to represent path-based link prediction, which we further study here to develop a more optimized quantum algorithm. Using a sampling framework for link prediction, we analyze the query access to the input network required to produce a certain number of prediction samples. Considering both well-known classical path-based algorithms using powers of the adjacency matrix as well as our proposed quantum algorithm for path-based link prediction, we argue that there is a polynomial quantum advantage on the dependence on N, the number of nodes in the network. We further argue that the complexity of our algorithm, although sub-linear in N, is limited by the complexity of performing a quantum simulation of the network's adjacency matrix, which may prove to be an important problem in the development of quantum algorithms for network science in general.
Collapse
Affiliation(s)
- João P Moutinho
- Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
- Instituto de Telecomunicações, Lisboa, Portugal.
| | - Duarte Magano
- Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
| | | |
Collapse
|
6
|
Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
Collapse
Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| |
Collapse
|
7
|
Abstract
Link prediction in complex network is an important issue in network science. Recently, various structure-based similarity methods have been proposed. Most of algorithms are used to analyze the topology of the network, and to judge whether there is any connection between nodes by calculating the similarity of two nodes. However, it is necessary to get the extra attribute information of the node in advance, which is very difficult. Compared to the difficulty in obtaining the attribute information of the node itself, the topology of the network is easy to obtain, and the structure of the network is an inherent attribute of the network and is more reliable. The proposed method measures kinds of similarity between nodes based on non-trivial eigenvectors of Laplacian Matrix of the network, such as Euclidean distance, Manhattan distance and Angular distance. Then the classical machine learning algorithm can be used for classification prediction (two classification in this case), so as to achieve the purpose of link prediction. Based on this process, a spectral analysis-based link prediction algorithm is proposed, and named it LPbSA (Link Prediction based on Spectral Analysis). The experimental results on seven real-world networks demonstrated that LPbSA has better performance on Accuracy, Precision, Receiver Operating Curve(ROC), area under the ROC curve(AUC), Precision and Recall curve(PR curve) and balanced F Score(F-score curve) evaluation metrics than other ten classic methods.
Collapse
Affiliation(s)
- Chun Gui
- College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China
- Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China
| |
Collapse
|
8
|
Sales-Pardo M, Mariné-Tena A, Guimerà R. Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networks. Proc Natl Acad Sci U S A 2023; 120:e2303887120. [PMID: 38060555 PMCID: PMC10723119 DOI: 10.1073/pnas.2303887120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/02/2023] [Indexed: 12/17/2023] Open
Abstract
Complex networked systems often exhibit higher-order interactions, beyond dyadic interactions, which can dramatically alter their observed behavior. Consequently, understanding hypergraphs from a structural perspective has become increasingly important. Statistical, group-based inference approaches are well suited for unveiling the underlying community structure and predicting unobserved interactions. However, these approaches often rely on two key assumptions: that the same groups can explain hyperedges of any order and that interactions are assortative, meaning that edges are formed by nodes with the same group memberships. To test these assumptions, we propose a group-based generative model for hypergraphs that does not impose an assortative mechanism to explain observed higher-order interactions, unlike current approaches. Our model allows us to explore the validity of the assumptions. Our results indicate that the first assumption appears to hold true for real networks. However, the second assumption is not necessarily accurate; we find that a combination of general statistical mechanisms can explain observed hyperedges. Finally, with our approach, we are also able to determine the importance of lower and high-order interactions for predicting unobserved interactions. Our research challenges the conventional assumptions of group-based inference methodologies and broadens our understanding of the underlying structure of hypergraphs.
Collapse
Affiliation(s)
- Marta Sales-Pardo
- Department of Chemical Engineering, Universitat Rovira i Virgili, TarragonaE-43007, Spain
| | | | - Roger Guimerà
- Department of Chemical Engineering, Universitat Rovira i Virgili, TarragonaE-43007, Spain
- Institució Catalana de Recerca i Estudis Avançats, BarcelonaE-08010, Spain
| |
Collapse
|
9
|
Andres G, Casiraghi G, Vaccario G, Schweitzer F. Reconstructing signed relations from interaction data. Sci Rep 2023; 13:20689. [PMID: 38001327 PMCID: PMC10673950 DOI: 10.1038/s41598-023-47822-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023] Open
Abstract
Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in five communities. We then show that these relations correspond to the ones reported by the individuals themselves. Additionally, using inferred relations, we study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. Finally, we study group cohesion in the analyzed communities by evaluating triad statistics in the reconstructed signed network.
Collapse
Affiliation(s)
- Georges Andres
- ETH Zürich, Chair of Systems Design, Weinbergstrasse 56/58, Zürich, Switzerland
| | - Giona Casiraghi
- ETH Zürich, Chair of Systems Design, Weinbergstrasse 56/58, Zürich, Switzerland
| | - Giacomo Vaccario
- ETH Zürich, Chair of Systems Design, Weinbergstrasse 56/58, Zürich, Switzerland
| | - Frank Schweitzer
- ETH Zürich, Chair of Systems Design, Weinbergstrasse 56/58, Zürich, Switzerland.
| |
Collapse
|
10
|
Chacko TP, Toole JT, Morris MC, Page J, Forsten RD, Barrett JP, Reinhard MJ, Brewster RC, Costanzo ME, Broderick G. A regulatory pathway model of neuropsychological disruption in Havana syndrome. Front Psychiatry 2023; 14:1180929. [PMID: 37965360 PMCID: PMC10642174 DOI: 10.3389/fpsyt.2023.1180929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 09/29/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction In 2016 diplomatic personnel serving in Havana, Cuba, began reporting audible sensory phenomena paired with onset of complex and persistent neurological symptoms consistent with brain injury. The etiology of these Anomalous Health Incidents (AHI) and subsequent symptoms remains unknown. This report investigates putative exposure-symptom pathology by assembling a network model of published bio-behavioral pathways and assessing how dysregulation of such pathways might explain loss of function in these subjects using data available in the published literature. Given similarities in presentation with mild traumatic brain injury (mTBI), we used the latter as a clinically relevant means of evaluating if the neuropsychological profiles observed in Havana Syndrome Havana Syndrome might be explained at least in part by a dysregulation of neurotransmission, neuro-inflammation, or both. Method Automated text-mining of >9,000 publications produced a network consisting of 273 documented regulatory interactions linking 29 neuro-chemical markers with 9 neuropsychological constructs from the Brief Mood Survey, PTSD Checklist, and the Frontal Systems Behavior Scale. Analysis of information flow through this network produced a set of regulatory rules reconciling to within a 6% departure known mechanistic pathways with neuropsychological profiles in N = 6 subjects. Results Predicted expression of neuro-chemical markers that jointly satisfy documented pathways and observed symptom profiles display characteristically elevated IL-1B, IL-10, NGF, and norepinephrine levels in the context of depressed BDNF, GDNF, IGF1, and glutamate expression (FDR < 5%). Elevations in CRH and IL-6 were also predicted unanimously across all subjects. Furthermore, simulations of neurological regulatory dynamics reveal subjects do not appear to be "locked in" persistent illness but rather appear to be engaged in a slow recovery trajectory. Discussion This computational analysis of measured neuropsychological symptoms in Havana-based diplomats proposes that these AHI symptoms may be supported in part by disruption of known neuroimmune and neurotransmission regulatory mechanisms also associated with mTBI.
Collapse
Affiliation(s)
- Thomas P. Chacko
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - J. Tory Toole
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Matthew C. Morris
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Jeffrey Page
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Robert D. Forsten
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
| | - John P. Barrett
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
- Department of Preventive Medicine and Biostatistics, Uniformed Services University, Bethesda, MD, United States
| | - Matthew J. Reinhard
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
- Complex Exposures Threats Center, Department of Veterans Affairs, Washington, DC, United States
| | - Ryan C. Brewster
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
| | - Michelle E. Costanzo
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
- Complex Exposures Threats Center, Department of Veterans Affairs, Washington, DC, United States
- Department of Medicine, Uniformed Services University, Bethesda, MD, United States
| | - Gordon Broderick
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- Complex Exposures Threats Center, Department of Veterans Affairs, Washington, DC, United States
| |
Collapse
|
11
|
Liu Z, Pan L, Chen G. Link-Information Augmented Twin Autoencoders for Network Denoising. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:5585-5595. [PMID: 35358055 DOI: 10.1109/tcyb.2022.3160470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Removing noisy links from an observed network is a task commonly required for preprocessing real-world network data. However, containing both noisy and clean links, the observed network cannot be treated as a trustworthy information source for supervised learning. Therefore, it is necessary but also technically challenging to detect noisy links in the context of data contamination. To address this issue, in the present article, a two-phased computational model is proposed, called link-information augmented twin autoencoders, which is able to deal with: 1) link information augmentation; 2) link-level contrastive denoising; 3) link information correction. Extensive experiments on six real-world networks verify that the proposed model outperforms other comparable methods in removing noisy links from the observed network so as to recover the real network from the corrupted one very accurately. Extended analyses also provide interpretable evidence to support the superiority of the proposed model for the task of network denoising.
Collapse
|
12
|
Peixoto TP, Kirkley A. Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection. Phys Rev E 2023; 108:024309. [PMID: 37723811 DOI: 10.1103/physreve.108.024309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 08/02/2023] [Indexed: 09/20/2023]
Abstract
The task of community detection, which aims to partition a network into clusters of nodes to summarize its large-scale structure, has spawned the development of many competing algorithms with varying objectives. Some community detection methods are inferential, explicitly deriving the clustering objective through a probabilistic generative model, while other methods are descriptive, dividing a network according to an objective motivated by a particular application, making it challenging to compare these methods on the same scale. Here we present a solution to this problem that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model. This allows us to compute the description length of a network and its partition under arbitrary objectives, providing a principled measure to compare the performance of different algorithms without the need for "ground-truth" labels. Our approach also gives access to instances of the community detection problem that are optimal to any given algorithm and in this way reveals intrinsic biases in popular descriptive methods, explaining their tendency to overfit. Using our framework, we compare a number of community detection methods on artificial networks and on a corpus of over 500 structurally diverse empirical networks. We find that more expressive community detection methods exhibit consistently superior compression performance on structured data instances, without having degraded performance on a minority of situations where more specialized algorithms perform optimally. Our results undermine the implications of the "no free lunch" theorem for community detection, both conceptually and in practice, since it is confined to unstructured data instances, unlike relevant community detection problems which are structured by requirement.
Collapse
Affiliation(s)
- Tiago P Peixoto
- Department of Network and Data Science, Central European University, 1100 Vienna, Austria
| | - Alec Kirkley
- Institute of Data Science, University of Hong Kong, Hong Kong; Department of Urban Planning and Design, University of Hong Kong, Hong Kong; and Urban Systems Institute, University of Hong Kong, Hong Kong
| |
Collapse
|
13
|
Li B, Yan T. Metagenomic next generation sequencing for studying antibiotic resistance genes in the environment. ADVANCES IN APPLIED MICROBIOLOGY 2023; 123:41-89. [PMID: 37400174 DOI: 10.1016/bs.aambs.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Bacterial antimicrobial resistance (AMR) is a persisting and growing threat to human health. Characterization of antibiotic resistance genes (ARGs) in the environment is important to understand and control ARG-associated microbial risks. Numerous challenges exist in monitoring ARGs in the environment, due to the extraordinary diversity of ARGs, low abundance of ARGs with respect to the complex environmental microbiomes, difficulties in linking ARGs with bacterial hosts by molecular methods, difficulties in achieving quantification and high throughput simultaneously, difficulties in assessing mobility potential of ARGs, and difficulties in determining the specific AMR determinant genes. Advances in the next generation sequencing (NGS) technologies and related computational and bioinformatic tools are facilitating rapid identification and characterization ARGs in genomes and metagenomes from environmental samples. This chapter discusses NGS-based strategies, including amplicon-based sequencing, whole genome sequencing, bacterial population-targeted metagenome sequencing, metagenomic NGS, quantitative metagenomic sequencing, and functional/phenotypic metagenomic sequencing. Current bioinformatic tools for analyzing sequencing data for studying environmental ARGs are also discussed.
Collapse
Affiliation(s)
- Bo Li
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Tao Yan
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, United States.
| |
Collapse
|
14
|
Moreno F, Galvis J, Gómez F. A foot and mouth disease ranking of risk using cattle transportation. PLoS One 2023; 18:e0284180. [PMID: 37053149 PMCID: PMC10101471 DOI: 10.1371/journal.pone.0284180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/24/2023] [Indexed: 04/14/2023] Open
Abstract
Foot-and-mouth disease (FMD) is a highly infectious condition that affects domestic and wild cloven-hoofed animals. This disease has substantial economic consequences. Livestock movement is one of the primary causes of disease dissemination. The centrality properties of the livestock mobilization transportation network provide valuable information for surveillance and control of FMD. However, the same transportation network can be described by different centrality descriptions, making it challenging to prioritize the most vulnerable nodes in the transportation network. This work considers the construction of a single network risk ranking, which helps prioritize disease control measurements. Results show that the proposed ranking constructed on 2016 livestock mobilization data may predict an actual outbreak reported in the Cesar (Colombia) region in 2018, with a performance measured by the area under the receiver operating characteristic curve of 0.91. This result constitutes the first quantitative evidence of the predictive capacity of livestock transportation to target FMD outbreaks. This approach may help decision-makers devise strategies to control and prevent FMD.
Collapse
Affiliation(s)
- Fausto Moreno
- Facultad de Medicina Veterinaria y de Zootecnia, Departamento de Producción Animal, Universidad Nacional de Colombia, Bogotá, Colombia
- Laboratorio de Analítica de Datos (Datalab), Universidad Nacional de Colombia, Bogotá, Colombia
| | - Juan Galvis
- Facultad de Ciencias, Departamento de Matemáticas, Universidad Nacional de Colombia, Bogotá, Colombia
- Laboratorio de Analítica de Datos (Datalab), Universidad Nacional de Colombia, Bogotá, Colombia
| | - Francisco Gómez
- Facultad de Ciencias, Departamento de Matemáticas, Universidad Nacional de Colombia, Bogotá, Colombia
- Laboratorio de Analítica de Datos (Datalab), Universidad Nacional de Colombia, Bogotá, Colombia
| |
Collapse
|
15
|
Ruiz-García M, Ozaita J, Pereda M, Alfonso A, Brañas-Garza P, Cuesta JA, Sánchez A. Triadic influence as a proxy for compatibility in social relationships. Proc Natl Acad Sci U S A 2023; 120:e2215041120. [PMID: 36947512 PMCID: PMC10068781 DOI: 10.1073/pnas.2215041120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric-the "triadic influence"-that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks-functions of the triadic influence and the personalities of the students-control the evolution of real social networks, opening an avenue for the quantitative study of these systems.
Collapse
Affiliation(s)
- Miguel Ruiz-García
- Departamento de Estructura de la Materia, Física Térmica y Electrónica, Universidad Complutense Madrid, Madrid28040, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
| | - Juan Ozaita
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
| | - María Pereda
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Grupo de Investigación Ingeniería de Organización y Logística (IOL), Departamento Ingeniería de Organización, Administración de empresas y Estadística, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid28006, Spain
| | - Antonio Alfonso
- LoyolaBehLAB, Department of Economics and Fundación ETEA, Universidad Loyola Andalucía, Córdoba14004, Spain
| | - Pablo Brañas-Garza
- LoyolaBehLAB, Department of Economics and Fundación ETEA, Universidad Loyola Andalucía, Córdoba14004, Spain
| | - José A. Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza50018, Spain
| | - Angel Sánchez
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza50018, Spain
| |
Collapse
|
16
|
Wang XW, Madeddu L, Spirohn K, Martini L, Fazzone A, Becchetti L, Wytock TP, Kovács IA, Balogh OM, Benczik B, Pétervári M, Ágg B, Ferdinandy P, Vulliard L, Menche J, Colonnese S, Petti M, Scarano G, Cuomo F, Hao T, Laval F, Willems L, Twizere JC, Vidal M, Calderwood MA, Petrillo E, Barabási AL, Silverman EK, Loscalzo J, Velardi P, Liu YY. Assessment of community efforts to advance network-based prediction of protein-protein interactions. Nat Commun 2023; 14:1582. [PMID: 36949045 PMCID: PMC10033937 DOI: 10.1038/s41467-023-37079-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/02/2023] [Indexed: 03/24/2023] Open
Abstract
Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
Collapse
Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Lorenzo Madeddu
- Translational and Precision Medicine Department Sapienza University of Rome, Rome, Italy
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | | | - Luca Becchetti
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | - Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
| | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, 60208, USA
| | - Olivér M Balogh
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
| | - Bettina Benczik
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Mátyás Pétervári
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
| | - Bence Ágg
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Péter Ferdinandy
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Loan Vulliard
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Stefania Colonnese
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Manuela Petti
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | - Gaetano Scarano
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Francesca Cuomo
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Laboratory of Molecular and Cellular Epigenetic, GIGA Institute, University of Liège, Liège, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Luc Willems
- Laboratory of Molecular and Cellular Epigenetic, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Enrico Petrillo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Albert-László Barabási
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Paola Velardi
- Translational and Precision Medicine Department Sapienza University of Rome, Rome, Italy.
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
| |
Collapse
|
17
|
Khaksar Manshad M, Meybodi MR, Salajegheh A. New Cellular Learning Automata as a framework for online link prediction problem. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2188261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Affiliation(s)
- Mozhdeh Khaksar Manshad
- Department of Computer Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
| | - Mohammad Reza Meybodi
- Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Afshin Salajegheh
- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| |
Collapse
|
18
|
The structure of biological complexity: Comment on "Networks behind the morphology and structural design of living systems" by Gosak et al. Phys Life Rev 2023; 44:73-76. [PMID: 36543074 DOI: 10.1016/j.plrev.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
|
19
|
Zhang C, Li Q, Lei Y, Qian M, Shen X, Cheng D, Yu W. The Absence of a Weak-Tie Effect When Predicting Large-Weight Links in Complex Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:422. [PMID: 36981311 PMCID: PMC10047936 DOI: 10.3390/e25030422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Link prediction is a hot issue in information filtering. Link prediction algorithms, based on local similarity indices, are widely used in many fields due to their high efficiency and high prediction accuracy. However, most existing link prediction algorithms are available for unweighted networks, and there are relatively few studies for weighted networks. In the previous studies on weighted networks, some scholars pointed out that links with small weights play a more important role in link prediction and emphasized that weak-ties theory has a significant impact on prediction accuracy. On this basis, we studied the edges with different weights, and we discovered that, for edges with large weights, this weak-ties theory actually does not work; Instead, the weak-ties theory works in the prediction of edges with small weights. Our discovery has instructive implications for link predictions in weighted networks.
Collapse
Affiliation(s)
- Chengjun Zhang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Qi Li
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yi Lei
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ming Qian
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xinyu Shen
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Di Cheng
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Wenbin Yu
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CI-CAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
| |
Collapse
|
20
|
Fundamental limits to learning closed-form mathematical models from data. Nat Commun 2023; 14:1043. [PMID: 36823107 PMCID: PMC9950473 DOI: 10.1038/s41467-023-36657-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which in this particular problem are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection.
Collapse
|
21
|
Liang K, Tan J, Zeng D, Huang Y, Huang X, Tan G. ABSLearn: a GNN-based framework for aliasing and buffer-size information retrieval. Pattern Anal Appl 2023. [DOI: 10.1007/s10044-023-01142-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
|
22
|
Mi Q, Wang X, Lin Y. A double attention graph network for link prediction on temporal graph. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
|
23
|
Vallès-Català T, Palau R. Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation. PLoS One 2023; 18:e0280604. [PMID: 36920915 PMCID: PMC10016679 DOI: 10.1371/journal.pone.0280604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 01/04/2023] [Indexed: 03/16/2023] Open
Abstract
For some decades now, theories on learning methodologies have advocated collaborative learning due to its good results in terms of effectiveness and learning types and its promotion of educational and social values. This means that teachers need to be able to apply different criteria when forming heterogeneous groups of students and to use automated techniques to assist them. In this study, we have created an approach based on complex network theory to design an algorithm called Minimum Entropy Collaborative Groupings (MECG) in order to form these heterogeneous groups more effectively. The algorithm was tested firstly under a synthetic framework and secondly in a real situation. In the first case, we generated 30 synthetic classrooms of different sizes and compared our approach with a genetic algorithm and a random grouping. In the latter case, the approach was tested on a group of 200 students on two subjects of a master's degree in teacher training. For each subject there were 4 large groups of 50 students each, in which collaborative groups of 4 students were created. Two of these large groups were used as random groups, another group used the CHAEA test and the fourth group used the LML test. The results showed that the groups created with MECG were more effective, had less uncertainty and were more interrelated and mature. It was observed that the randomized groups did not obtain significantly better LML results and that this cannot be related to any emotional or motivational effect because the students performed the test as a placebo measure. In terms of learning styles, the results were significantly better with LML than with CHAEA, whereas no significant difference was observed in the randomized groups.
Collapse
Affiliation(s)
- Toni Vallès-Català
- Centre d’Estudis Superiors de l’Aviació (CESDA), Reus, Catalonia, Spain
- * E-mail:
| | - Ramon Palau
- ARGET Research Group, Faculty of Education Sciences and Psychology, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
| |
Collapse
|
24
|
Kuang J, Scoglio C, Michel K. Feature learning and network structure from noisy node activity data. Phys Rev E 2022; 106:064301. [PMID: 36671154 PMCID: PMC9869472 DOI: 10.1103/physreve.106.064301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data. First, we design a scheme to generate random node sequences from node context sets, which are generated from node activity data. Then, a three-layer neural network is adopted training the node sequences to obtain node vectors, which allow us to construct networks and capture nodes with synergistic roles. Furthermore, we present an entropy-based approach to select the most meaningful neighbors for each node in the resulting network. Finally, the effectiveness of the method is validated through both synthetic and real data.
Collapse
Affiliation(s)
- Junyao Kuang
- Department of Electrical and Computer Engineering
| | | | | |
Collapse
|
25
|
Peel L, Peixoto TP, De Domenico M. Statistical inference links data and theory in network science. Nat Commun 2022; 13:6794. [PMID: 36357376 PMCID: PMC9649740 DOI: 10.1038/s41467-022-34267-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications. Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.
Collapse
|
26
|
Wang P, Wu C, Huang T, Chen Y. A Supervised Link Prediction Method Using Optimized Vertex Collocation Profile. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1465. [PMID: 37420484 DOI: 10.3390/e24101465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 07/09/2023]
Abstract
Classical link prediction methods mainly utilize vertex information and topological structure to predict missing links in networks. However, accessing vertex information in real-world networks, such as social networks, is still challenging. Moreover, link prediction methods based on topological structure are usually heuristic, and mainly consider common neighbors, vertex degrees and paths, which cannot fully represent the topology context. In recent years, network embedding models have shown efficiency for link prediction, but they lack interpretability. To address these issues, this paper proposes a novel link prediction method based on an optimized vertex collocation profile (OVCP). First, the 7-subgraph topology was proposed to represent the topology context of vertexes. Second, any 7-subgraph can be converted into a unique address by OVCP, and then we obtained the interpretable feature vectors of vertexes. Third, the classification model with OVCP features was used to predict links, and the overlapping community detection algorithm was employed to divide a network into multiple small communities, which can greatly reduce the complexity of our method. Experimental results demonstrate that the proposed method can achieve a promising performance compared with traditional link prediction methods, and has better interpretability than network-embedding-based methods.
Collapse
Affiliation(s)
- Peng Wang
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
- School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
| | - Chenxiao Wu
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
- Chien-Shiung Wu College, Southeast University, Nanjing 211189, China
| | - Teng Huang
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
- Chien-Shiung Wu College, Southeast University, Nanjing 211189, China
| | - Yizhang Chen
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
| |
Collapse
|
27
|
Cummins B, Motta FC, Moseley RC, Deckard A, Campione S, Gameiro M, Gedeon T, Mischaikow K, Haase SB. Experimental guidance for discovering genetic networks through hypothesis reduction on time series. PLoS Comput Biol 2022; 18:e1010145. [PMID: 36215333 PMCID: PMC9584434 DOI: 10.1371/journal.pcbi.1010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/20/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022] Open
Abstract
Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.
Collapse
Affiliation(s)
- Breschine Cummins
- Department of Mathematical Sciences, Montana State University, Bozeman, Montana, United States of America
- * E-mail:
| | - Francis C. Motta
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, Florida, United States of America
| | - Robert C. Moseley
- Department of Biology, Duke University, Durham, North Carolina, United States of America
| | - Anastasia Deckard
- Geometric Data Analytics, Durham, North Carolina, United States of America
| | - Sophia Campione
- Department of Biology, Duke University, Durham, North Carolina, United States of America
| | - Marcio Gameiro
- Department of Mathematics, Rutgers University, New Brunswick, New Jersey, United States of America
- Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, São Paulo, Brazil
| | - Tomáš Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, Montana, United States of America
| | - Konstantin Mischaikow
- Department of Mathematics, Rutgers University, New Brunswick, New Jersey, United States of America
| | - Steven B. Haase
- Department of Biology, Duke University, Durham, North Carolina, United States of America
| |
Collapse
|
28
|
Shen Y, Jiang X, Li Z, Wang Y, Jin X, Ma S, Cheng X. NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups. Knowl Inf Syst 2022; 64:2771-2795. [PMID: 36035894 PMCID: PMC9395818 DOI: 10.1007/s10115-022-01724-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 11/05/2022]
Abstract
Real-world network data consisting of social interactions can be incomplete due to deliberately erased or unsuccessful data collection, which cause the misleading of social interaction analysis for many various time-aware applications. Naturally, the link prediction task has drawn much research interest to predict the missing edges in the incomplete social network. However, existing studies of link prediction cannot effectively capture the entangling topological and temporal dynamics already residing in the social network, thus cannot effectively reasoning the missing interactions in dynamic networks. In this paper, we propose the NEAWalk, a novel model to infer the missing social interaction based on topological-temporal features of patterns in the social group. NEAWalk samples the query-relevant walks containing both the historical and evolving information by focusing on the temporal constraint and designs a dual-view anonymization procedure for extracting both topological and temporal features from the collected walks to conduct the inference. Two-track experiments on several well-known network datasets demonstrate that the NEAWalk stably achieves superior performance against several state-of-the-art baseline methods.
Collapse
|
29
|
Lv J, Liu G, Hao J, Ju Y, Sun B, Sun Y. Computational models, databases and tools for antibiotic combinations. Brief Bioinform 2022; 23:6652783. [PMID: 35915052 DOI: 10.1093/bib/bbac309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.
Collapse
Affiliation(s)
- Ji Lv
- College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Guixia Liu
- College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Junli Hao
- College of Food Science, Northeast Agricultural University, Harbin, China
| | - Yuan Ju
- Sichuan University Library, Sichuan University, Chengdu, China
| | - Binwen Sun
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumor Therapy, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ying Sun
- Department of Respiratory Medicine, the First Hospital of Jilin University, Changchun, China
| |
Collapse
|
30
|
Runghen R, Stouffer DB, Dalla Riva GV. Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220079. [PMID: 36016910 PMCID: PMC9399714 DOI: 10.1098/rsos.220079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Networks are increasingly used in various fields to represent systems with the aim of understanding the underlying rules governing observed interactions, and hence predict how the system is likely to behave in the future. Recent developments in network science highlight that accounting for node metadata improves both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, to predict interactions in a network within existing statistical and machine learning frameworks, we need to learn objects that rapidly grow in dimension with the number of nodes. Thus, the task becomes computationally and conceptually challenging for networks. Here, we present a new predictive procedure combining a statistical, low-rank graph embedding method with machine learning techniques which reduces substantially the complexity of the learning task and allows us to efficiently predict interactions from node metadata in bipartite networks. To illustrate its application on real-world data, we apply it to a large dataset of tourist visits across a country. We found that our procedure accurately reconstructs existing interactions and predicts new interactions in the network. Overall, both from a network science and data science perspective, our work offers a flexible and generalizable procedure for link prediction.
Collapse
Affiliation(s)
- Rogini Runghen
- Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
- The Roux Institute, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Daniel B. Stouffer
- Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
| | - Giulio V. Dalla Riva
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| |
Collapse
|
31
|
Chacko TP, Toole JT, Richman S, Spink GL, Reinhard MJ, Brewster RC, Costanzo ME, Broderick G. Mapping the network biology of metabolic response to stress in posttraumatic stress disorder and obesity. Front Psychol 2022; 13:941019. [PMID: 35959009 PMCID: PMC9362840 DOI: 10.3389/fpsyg.2022.941019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
The co-occurrence of stress-induced posttraumatic stress disorder (PTSD) and obesity is common, particularly among military personnel but the link between these conditions is unclear. Individuals with comorbid PTSD and obesity manifest other physical and psychological problems, which significantly diminish their quality of life. Current understanding of the pathways connecting stress to PTSD and obesity is focused largely on behavioral mediators alone with little consideration of the biological regulatory mechanisms that underlie their co-occurrence. In this work, we leverage prior knowledge to systematically highlight such bio-behavioral mechanisms and inform on the design of confirmatory pilot studies. We use natural language processing (NLP) to extract documented regulatory interactions involved in the metabolic response to stress and its impact on obesity and PTSD from over 8 million peer-reviewed papers. The resulting network describes the propagation of stress to PTSD and obesity through 34 metabolic mediators using 302 documented regulatory interactions supported by over 10,000 citations. Stress jointly affected both conditions through 21 distinct pathways involving only two intermediate metabolic mediators out of a total of 76 available paths through this network. Moreover, oxytocin (OXT), Neuropeptide-Y (NPY), and cortisol supported an almost direct propagation of stress to PTSD and obesity with different net effects. Although stress upregulated both NPY and cortisol, the downstream effects of both markers are reported to relieve PTSD severity but exacerbate obesity. The stress-mediated release of oxytocin, however, was found to concurrently downregulate the severity of both conditions. These findings highlight how a network-informed approach that leverages prior knowledge might be used effectively in identifying key mediators like OXT though experimental verification of signal transmission dynamics through each path will be needed to determine the actual likelihood and extent of each marker’s participation.
Collapse
Affiliation(s)
- Thomas P. Chacko
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- Institute of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States
| | - J. Tory Toole
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- Institute of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States
| | - Spencer Richman
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Garry L. Spink
- Rochester Regional Behavioral Health, Rochester, NY, United States
| | - Matthew J. Reinhard
- War Related Illness and Injury Study Center, United States Department of Veterans Affairs, Washington, DC, United States
| | - Ryan C. Brewster
- War Related Illness and Injury Study Center, United States Department of Veterans Affairs, Washington, DC, United States
| | - Michelle E. Costanzo
- War Related Illness and Injury Study Center, United States Department of Veterans Affairs, Washington, DC, United States
| | - Gordon Broderick
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- *Correspondence: Gordon Broderick,
| |
Collapse
|
32
|
Wang H, Yang W, Wang W, Man D, Lv J. A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks. ACM TRANSACTIONS ON PRIVACY AND SECURITY 2022. [DOI: 10.1145/3548685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability and robustness for the scenarios with evasion strategies.
Collapse
Affiliation(s)
| | | | | | | | - Jiguang Lv
- College of Computer Science and Technology, Harbin Engineering University, China
| |
Collapse
|
33
|
Wu H, Song C, Ge Y, Ge T. Link Prediction on Complex Networks: An Experimental Survey. DATA SCIENCE AND ENGINEERING 2022; 7:253-278. [PMID: 35754861 PMCID: PMC9211798 DOI: 10.1007/s41019-022-00188-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/31/2022] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.
Collapse
Affiliation(s)
- Haixia Wu
- College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University, Tianjin, China
| | - Chunyao Song
- College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University, Tianjin, China
| | - Yao Ge
- College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University, Tianjin, China
| | - Tingjian Ge
- University of Massachusetts Lowell, Massachusetts, United States
| |
Collapse
|
34
|
Saavedra S, Bartomeus I, Godoy O, Rohr RP, Zu P. Towards a system-level causative knowledge of pollinator communities. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210159. [PMID: 35491588 PMCID: PMC9058529 DOI: 10.1098/rstb.2021.0159] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Pollination plays a central role in both crop production and maintaining biodiversity. However, habitat loss, pesticides, invasive species and larger environmental fluctuations are contributing to a dramatic decline of pollinators worldwide. Different management solutions require knowledge of how ecological communities will respond following interventions. Yet, anticipating the response of these systems to interventions remains extremely challenging due to the unpredictable nature of ecological communities, whose nonlinear behaviour depends on the specific details of species interactions and the various unknown or unmeasured confounding factors. Here, we propose that this knowledge can be derived by following a probabilistic systems analysis rooted on non-parametric causal inference. The main outcome of this analysis is to estimate the extent to which a hypothesized cause can increase or decrease the probability that a given effect happens without making assumptions about the form of the cause-effect relationship. We discuss a road map for how this analysis can be accomplished with the aim of increasing our system-level causative knowledge of natural communities. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.
Collapse
Affiliation(s)
- Serguei Saavedra
- Department of Civil and Environmental Engineering, MIT, 77 Massachusetts Av., Cambridge, MA 02139, USA
| | - Ignasi Bartomeus
- Estación Biológica de Doñana (EBD-CSIC), 41092, Isla de la Cartuja, Seville, Spain
| | - Oscar Godoy
- Departamento de Biología, Instituto Universitario de Ciencias del Mar (INMAR), Universidad de Cádiz, Royal Port E-11510, Spain
| | - Rudolf P. Rohr
- Department of Biology - Ecology and Evolution, University of Fribourg, Chemin du Musée 10, Fribourg CH-1700, Switzerland
| | - Penguan Zu
- Department of Environmental Systems Science, ETH Zurich, Schmelzbergstrasse 9, Zurich CH-8092, Switzerland,Department Fish Ecology and Evolution, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Seestrasse 79, Kastanienbaum CH-6047, Switzerland
| |
Collapse
|
35
|
Mori L, O’Hara K, Pujol TA, Ventresca M. Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata. ENTROPY 2022; 24:e24060842. [PMID: 35741562 PMCID: PMC9223064 DOI: 10.3390/e24060842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/08/2022] [Accepted: 06/14/2022] [Indexed: 02/01/2023]
Abstract
With the goal of understanding if the information contained in node metadata can help in the task of link weight prediction, we investigate herein whether incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link improves the prediction accuracy of common supervised machine learning methods. In contrast with previous works, instead of normalizing the link weights, we treat them as count variables representing the number of interactions between end nodes, as this is a natural representation for many datasets in the literature. In this preliminary study, we find no significant evidence that metadata similarity improved the prediction accuracy of the four empirical datasets studied. To further explore the role of node metadata in weight prediction, we synthesized weights to analyze the extreme case where the weights depend solely on the metadata of the end nodes, while encoding different relationships between them using logical operators in the generation process. Under these conditions, the random forest method performed significantly better than other methods in 99.07% of cases, though the prediction accuracy was significantly degraded for the methods analyzed in comparison to the experiments with the original weights.
Collapse
Affiliation(s)
- Larissa Mori
- School of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA; (L.M.); (K.O.)
| | - Kaleigh O’Hara
- School of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA; (L.M.); (K.O.)
| | | | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA; (L.M.); (K.O.)
- Correspondence:
| |
Collapse
|
36
|
Allegri SA, McCoy K, Mitchell CS. CompositeView: A Network-Based Visualization Tool. BIG DATA AND COGNITIVE COMPUTING 2022; 6. [PMID: 35847767 PMCID: PMC9281616 DOI: 10.3390/bdcc6020066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi.
Collapse
Affiliation(s)
- Stephen A. Allegri
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Kevin McCoy
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Cassie S. Mitchell
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Machine Learning Center at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence:
| |
Collapse
|
37
|
Haj Ali S, Hütt MT. Inferring missing edges in a graph from observed collective patterns. Phys Rev E 2022; 105:064610. [PMID: 35854582 DOI: 10.1103/physreve.105.064610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Many real-life networks are incomplete. Dynamical observations can allow estimating missing edges. Such procedures, often summarized under the term 'network inference', typically evaluate the statistical correlations among pairs of nodes to determine connectivity. Here, we offer an alternative approach: completing an incomplete network by observing its collective behavior. We illustrate this approach for the case of patterns emerging in reaction-diffusion systems on graphs, where collective behaviors can be associated with eigenvectors of the network's Laplacian matrix. Our method combines a partial spectral decomposition of the network's Laplacian matrix with eigenvalue assignment by matching the patterns to the eigenvectors of the incomplete graph. We show that knowledge of a few collective patterns can allow the prediction of missing edges and that this result holds across a range of network architectures. We present a numerical case study using activator-inhibitor dynamics and we illustrate that the main requirement for the observed patterns is that they are not confined to subsets of nodes, but involve the whole network.
Collapse
Affiliation(s)
- Selim Haj Ali
- Department of Life Sciences and Chemistry, Jacobs University Bremen, D-28759 Bremen, Germany
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, D-28759 Bremen, Germany
| |
Collapse
|
38
|
HOPLP − MUL: link prediction in multiplex networks based on higher order paths and layer fusion. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03733-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
39
|
Cobo-López S, Gupta VK, Sung J, Guimerà R, Sales-Pardo M. Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes. PNAS NEXUS 2022; 1:pgac055. [PMID: 36741465 PMCID: PMC9896942 DOI: 10.1093/pnasnexus/pgac055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 05/10/2022] [Indexed: 02/07/2023]
Abstract
A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.
Collapse
Affiliation(s)
- Sergio Cobo-López
- Departament d’Enginyeria Química, Universitat Rovira i Virgili, 40007 Tarragona, Catalonia, Spain
| | - Vinod K Gupta
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA,Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | |
Collapse
|
40
|
Xu H, Yue Z, Pang H, Elahi E, Li J, Wang L. Integrative model for discovering linked topics in science and technology. J Informetr 2022. [DOI: 10.1016/j.joi.2022.101265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
41
|
Vaca-Ramírez F, Peixoto TP. Systematic assessment of the quality of fit of the stochastic block model for empirical networks. Phys Rev E 2022; 105:054311. [PMID: 35706168 DOI: 10.1103/physreve.105.054311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.
Collapse
Affiliation(s)
- Felipe Vaca-Ramírez
- Department of Network and Data Science, Central European University, 1100 Vienna, Austria
| | - Tiago P Peixoto
- Department of Network and Data Science, Central European University, 1100 Vienna, Austria
| |
Collapse
|
42
|
Li L, Wen Y, Bai S, Liu P. Link prediction in weighted networks via motif predictor. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
43
|
Shang KK, Small M. Link prediction for long-circle-like networks. Phys Rev E 2022; 105:024311. [PMID: 35291151 DOI: 10.1103/physreve.105.024311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Link prediction is the problem of predicting the uncertain relationship between a pair of nodes from observed structural information of a network. Link prediction algorithms are useful in gaining insight into different network structures from partial observation of exemplars. Existing local and quasilocal link prediction algorithms with low computational complexity focus on regular complex networks with sufficiently many closed triangular motifs or on tree-like networks with the vast majority of open triangular motifs. However, the three-node motif cannot describe the local structural features of all networks, and we find the main structure of many networks is long line or closed circle that cannot be predicted well via traditional link prediction algorithms. Meanwhile, some global link prediction algorithms are effective but accompanied by high computational complexity. In this paper, we proposed a local method that is based on the natural characteristic of a long line-in contrast to the preferential attachment principle. Next, we test our algorithms for two kinds of symbolic long-circle-like networks: a metropolitan water distribution network and a sexual contact network. We find that our method is effective and performs much better than many traditional local and global algorithms. We adopt the community detection method to improve the accuracy of our algorithm, which shows that the long-circle-like networks also have clear community structure. We further suggest that the structural features are key for the link prediction problem. Finally, we propose a long-line network model to prove that our core idea is of universal significance.
Collapse
Affiliation(s)
- Ke-Ke Shang
- Computational Communication Collaboratory, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Michael Small
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Crawley, Western Australia 6009, Australia
| |
Collapse
|
44
|
Suriyalaksh M, Raimondi C, Mains A, Segonds-Pichon A, Mukhtar S, Murdoch S, Aldunate R, Krueger F, Guimerà R, Andrews S, Sales-Pardo M, Casanueva O. Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes. iScience 2022; 25:103663. [PMID: 35036864 PMCID: PMC8753122 DOI: 10.1016/j.isci.2021.103663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/09/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
We design a “wisdom-of-the-crowds” GRN inference pipeline and couple it to complex network analysis to understand the organizational principles governing gene regulation in long-lived glp-1/Notch Caenorhabditis elegans. The GRN has three layers (input, core, and output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess the functional importance of structural layers, we screened 80% of regulators and discovered 50 new aging genes, 86% with human orthologues. Genes essential for longevity—including ones involved in insulin-like signaling (ILS)—are at the core, indicating that GRN's structure is predictive of functionality. We used in vivo reporters and a novel functional network covering 5,497 genetic interactions to make mechanistic predictions. We used genetic epistasis to test some of these predictions, uncovering a novel transcriptional regulator, sup-37, that works alongside DAF-16/FOXO. We present a framework with predictive power that can accelerate discovery in C. elegans and potentially humans. Gene-regulatory inference provides global network of long-lived animals The large-scale topology of the network has an hourglass structure Membership to the core of the hourglass is a good predictor of functionality Discovered 50 novel aging genes, including sup-37, a DAF-16 dependent gene
Collapse
Affiliation(s)
| | | | - Abraham Mains
- Babraham Institute, Babraham, Cambridge CB22 3AT, UK
| | | | | | | | - Rebeca Aldunate
- Escuela de Biotecnología, Facultad de Ciencias, Universidad Santo Tomas, Santiago, Chile
| | - Felix Krueger
- Babraham Institute, Babraham, Cambridge CB22 3AT, UK
| | - Roger Guimerà
- ICREA, Barcelona 08010, Catalonia, Spain.,Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| | - Simon Andrews
- Babraham Institute, Babraham, Cambridge CB22 3AT, UK
| | - Marta Sales-Pardo
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| | | |
Collapse
|
45
|
A novel graph mining approach to predict and evaluate food-drug interactions. Sci Rep 2022; 12:1061. [PMID: 35058561 PMCID: PMC8776972 DOI: 10.1038/s41598-022-05132-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/05/2022] [Indexed: 12/26/2022] Open
Abstract
Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.
Collapse
|
46
|
Chen M, Zhang Y, Zhang Z, Du L, Wang S, Zhang J. Inferring network structure with unobservable nodes from time series data. CHAOS (WOODBURY, N.Y.) 2022; 32:013126. [PMID: 35105141 DOI: 10.1063/5.0076521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Network structures play important roles in social, technological, and biological systems. However, the observable nodes and connections in real cases are often incomplete or unavailable due to measurement errors, private protection issues, or other problems. Therefore, inferring the complete network structure is useful for understanding human interactions and complex dynamics. The existing studies have not fully solved the problem of the inferring network structure with partial information about connections or nodes. In this paper, we tackle the problem by utilizing time series data generated by network dynamics. We regard the network inference problem based on dynamical time series data as a problem of minimizing errors for predicting states of observable nodes and proposed a novel data-driven deep learning model called Gumbel-softmax Inference for Network (GIN) to solve the problem under incomplete information. The GIN framework includes three modules: a dynamics learner, a network generator, and an initial state generator to infer the unobservable parts of the network. We implement experiments on artificial and empirical social networks with discrete and continuous dynamics. The experiments show that our method can infer the unknown parts of the structure and the initial states of the observable nodes with up to 90% accuracy. The accuracy declines linearly with the increase of the fractions of unobservable nodes. Our framework may have wide applications where the network structure is hard to obtain and the time series data is rich.
Collapse
Affiliation(s)
- Mengyuan Chen
- School of Systems Science, Beijing Normal University, No. 19, Xinjiekou Wai Street, Beijing 100875, China
| | - Yan Zhang
- School of Systems Science, Beijing Normal University, No. 19, Xinjiekou Wai Street, Beijing 100875, China
| | - Zhang Zhang
- School of Systems Science, Beijing Normal University, No. 19, Xinjiekou Wai Street, Beijing 100875, China
| | - Lun Du
- Microsoft Research, No. 5 Danling Street, Haidian District, Beijing 10080, China
| | - Shuo Wang
- School of Systems Science, Beijing Normal University, No. 19, Xinjiekou Wai Street, Beijing 100875, China
| | - Jiang Zhang
- School of Systems Science, Beijing Normal University, No. 19, Xinjiekou Wai Street, Beijing 100875, China
| |
Collapse
|
47
|
|
48
|
|
49
|
Abstract
Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.
Collapse
Affiliation(s)
- Tao Zhou
- CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
| |
Collapse
|
50
|
Ge J, Wang X, Shi W. Link prediction of the world container shipping network: A network structure perspective. CHAOS (WOODBURY, N.Y.) 2021; 31:113123. [PMID: 34881597 DOI: 10.1063/5.0056864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/14/2021] [Indexed: 06/13/2023]
Abstract
Although the world container shipping network (WCSN) has gradually been shaped with ever-increasing complexity in link evolution over the last decades, its evolving mechanism remains to be unveiled. This motivates us to explore the evolutionary pattern of the WCSN, which can be achieved by advancing the existing link prediction models. Using the k-shell decomposition method, the network hierarchy can be decomposed and evaluated by four indices which are KS-Salton, KS-AA, KS-RA, and KS-LRW. The results show that the network hierarchy depends largely on trade patterns and demonstrates certain geographic characteristics. Meanwhile, the KS-LRW index performs best and, therefore, is further simulated for the future WCSN by predicting its top 1677 potential edges, which significantly enhances the overall network connectivity and efficiency. These findings create profound implications for shipping companies to strategically reduce the trail cost for new lines by analyzing the network data.
Collapse
Affiliation(s)
- Jiawei Ge
- College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
| | - Xuefeng Wang
- College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
| | - Wenming Shi
- Maritime and Logistics Management, National Centre for Ports and Shipping, Australian Maritime College, University of Tasmania, Newnham Tasmania 7248, Australia
| |
Collapse
|