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Sun H, Yu H, Fan G, Chen L, Liu Z. Security-Aware and Time-Guaranteed Service Placement in Edge Clouds. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023. [DOI: 10.1109/tnsm.2022.3213761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Huaiying Sun
- Department of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China
| | - Huiqun Yu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Guisheng Fan
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Liqiong Chen
- Department of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China
| | - Zheng Liu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
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2
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A straightforward edge centrality concept derived from generalizing degree and strength. Sci Rep 2022; 12:4407. [PMID: 35292696 PMCID: PMC8922089 DOI: 10.1038/s41598-022-08254-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 12/24/2022] Open
Abstract
Vertex degree—the number of edges that are incident to a vertex—is a fundamental concept in network theory. It is the historically first and conceptually simplest centrality concept to rate the importance of a vertex for a network’s structure and dynamics. Unlike many other centrality concepts, for which joint metrics have been proposed for both vertices and edges, by now there is no concept for an edge centrality analogous to vertex degree. Here, we propose such a concept—termed nearest-neighbor edge centrality—and demonstrate its suitability for a non-redundant identification of central edges in paradigmatic network models as well as in real-world networks from various scientific domains.
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Tang Y, Yao Y, Wei G. Unraveling the Allosteric Mechanism of Four Cancer-related Mutations in the Disruption of p53-DNA Interaction. J Phys Chem B 2021; 125:10138-10148. [PMID: 34403252 DOI: 10.1021/acs.jpcb.1c05638] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The p53 protein plays active roles in the physiological regulation of cell cycle as well as in cancer developments. In more than half of human cancers, the protein is inactivated by mutations located primarily in its DNA-binding domain (DBD), and some mutations located in the β-sandwich region of DBD are reported to decrease p53-DNA binding affinities. To understand the long-range correlation between p53 β-sandwich and DNA, and the allosteric mechanism of β-sandwich mutations in the disruption of p53-DNA interactions, we first identify three regions with a strong correlation with DNA based on microsecond molecular dynamics (MD) simulations of wild-type p53-DNA complex and then perform multiple MD simulations on four cancer-related mutants L145Q, P151S, Y220C, and G266R, which are located in these three regions. Our simulations show that these mutations allosterically destabilize the structural stability of the DNA-binding groove in p53 and disrupt the p53-DNA interactions. Network analyses reveal optimal correlation paths through which the mutation-induced allosteric signal passes to DNA, and the disturbance effect of these mutations on the global connectivity and dynamical correlation of the p53-DNA complex. This work paves the way for the in-depth understanding of the mutation-induced loss in p53's DNA-recognition ability and the pathological mechanism of cancer development.
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Affiliation(s)
- Yiming Tang
- Department of Physics, State Key Laboratory of Surface physics, and Key Laboratory for Computational Physical Science (Ministry of Education), Fudan University, Shanghai 200433, People's Republic of China
| | - Yifei Yao
- Department of Physics, State Key Laboratory of Surface physics, and Key Laboratory for Computational Physical Science (Ministry of Education), Fudan University, Shanghai 200433, People's Republic of China
| | - Guanghong Wei
- Department of Physics, State Key Laboratory of Surface physics, and Key Laboratory for Computational Physical Science (Ministry of Education), Fudan University, Shanghai 200433, People's Republic of China
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Zangrossi A, Zanzotto G, Lorenzoni F, Indelicato G, Cannas Aghedu F, Cermelli P, Bisiacchi PS. Resting-state functional brain connectivity predicts cognitive performance: An exploratory study on a time-based prospective memory task. Behav Brain Res 2021; 402:113130. [PMID: 33444694 DOI: 10.1016/j.bbr.2021.113130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 12/22/2020] [Accepted: 01/05/2021] [Indexed: 11/16/2022]
Abstract
Resting-state functional brain connectivity (rsFC) is in wide use for the investigation of a variety of cognitive neuroscience phenomena. In the first phase of this study, we explored the changes in EEG-reconstructed rsFC in young vs. older adults, in the both the open-eyes (OE) and the closed-eyes (CE) conditions. The results showed significant differences in several rsFC network metrics in the two age groups, confirming and detailing established knowledge that aging modulates brain functional organisation. In the study's second phase we investigated the role of rsFC architecture on cognitive performance through a time-based Prospective Memory task involving participants who monitored the passage of time to perform a specific action at an appropriate time in the future. Regression models revealed that the monitoring strategy (i.e. the number of clock checks) can be predicted by rsFC graph metric, specifically, eccentricity and betweenness in the OE condition, and assortativity in the CE condition. These results show for the first time how metrics qualifying functional brain connectivity at rest can account for the differences in the way individuals strategically handle cognitive loads in the Prospective Memory domain.
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Affiliation(s)
- Andrea Zangrossi
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Padova, Italy.
| | - Giovanni Zanzotto
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Department of General Psychology, University of Padova, Padova, Italy
| | | | - Giuliana Indelicato
- York Cross-disciplinary Centre for Systems Analysis, Department of Mathematics, University of York, UK
| | | | | | - Patrizia Silvia Bisiacchi
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Department of General Psychology, University of Padova, Padova, Italy
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Tulu MM, Mkiramweni ME, Hou R, Feisso S, Younas T. Influential nodes selection to enhance data dissemination in mobile social networks: A survey. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2020; 169:102768. [DOI: 10.1016/j.jnca.2020.102768] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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6
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Zarei B, Meybodi MR. Detecting community structure in complex networks using genetic algorithm based on object migrating automata. Comput Intell 2020. [DOI: 10.1111/coin.12273] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Bagher Zarei
- Faculty of Computer and Information Technology EngineeringQazvin Branch, Islamic Azad University Qazvin Iran
| | - Mohammad Reza Meybodi
- Department of Computer Engineering and Information TechnologyAmirkabir University of Technology Tehran Iran
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Huang J, Hou Y, Li Y. Efficient community detection algorithm based on higher-order structures in complex networks. CHAOS (WOODBURY, N.Y.) 2020; 30:023114. [PMID: 32113221 DOI: 10.1063/1.5130523] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/14/2020] [Indexed: 06/10/2023]
Abstract
It is a challenging problem to assign communities in a complex network so that nodes in a community are tightly connected on the basis of higher-order connectivity patterns such as motifs. In this paper, we develop an efficient algorithm that detects communities based on higher-order structures. Our algorithm can also detect communities based on a signed motif, a colored motif, a weighted motif, as well as multiple motifs. We also introduce stochastic block models on the basis of higher-order structures. Then, we test our community detection algorithm on real-world networks and computer generated graphs drawn from the stochastic block models. The results of the tests indicate that our community detection algorithm is effective to identify communities on the basis of higher-order connectivity patterns.
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Affiliation(s)
- Jinyu Huang
- College of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, People's Republic of China
| | - Yani Hou
- College of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, People's Republic of China
| | - Yuansong Li
- College of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, People's Republic of China
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Bello R, Miao D, Falcon R, Nakata M, Rosete A, Ciucci D. Three-Way Decisions Community Detection Model Based on Weighted Graph Representation. ROUGH SETS 2020. [PMCID: PMC7338166 DOI: 10.1007/978-3-030-52705-1_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Community detection is of great significance to the study of complex networks. Community detection algorithm based on three-way decisions (TWD) forms a multi-layered community structure by hierarchical clustering and then selects a suitable layer as the community detection result. However, this layer usually contains overlapping communities. Based on the idea of TWD, we define the overlapping part in the communities as boundary region (BND), and the non-overlapping part as positive region (POS) or negative region (NEG). How to correctly divide the nodes in the BND into the POS or NEG is a challenge for three-way decisions community detection. The general methods to deal with boundary region are modularity increment and similarity calculation. But these methods only take advantage of the local features of the network, without considering the information of the divided communities and the similarity of the global structure. Therefore, in this paper, we propose a method for three-way decisions community detection based on weighted graph representation (WGR-TWD). The weighted graph representation (WGR) can well transform the global structure into vector representation and make the two nodes in the boundary region more similar by using frequency of appearing in the same community as the weight. Firstly, the multi-layered community structure is constructed by hierarchical clustering. The target layer is selected according to the extended modularity value of each layer. Secondly, all nodes are converted into vectors by WGR. Finally, the nodes in the BND are divided into the POS or NEG based on cosine similarity. Experiments on real-world networks demonstrate that WGR-TWD is effective for community detection in networks compared with the state-of-the-art algorithms.
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Affiliation(s)
- Rafael Bello
- Department of Computer Science, Universidad Central de Las Villas, Santa Clara, Cuba
| | - Duoqian Miao
- Department of Computer Science and Technology, Tongji University, Shanghai, China
| | - Rafael Falcon
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON Canada
| | - Michinori Nakata
- Department of Management and Information Science, Josai International University, Togane, Chiba Japan
| | - Alejandro Rosete
- Departamento de Inteligencia Artificial e Infraestructura de Sistemas Informáticos, Universidad Tecnológica de La Habana “José Antonio Echeverría” (CUJAE), Havana, Cuba
| | - Davide Ciucci
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
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Duan Z, Zou H, Min X, Zhao S, Chen J, Zhang Y. An adaptive granulation algorithm for community detection based on improved label propagation. Int J Approx Reason 2019. [DOI: 10.1016/j.ijar.2019.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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10
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Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning. BIG DATA AND COGNITIVE COMPUTING 2019. [DOI: 10.3390/bdcc3010010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Early language acquisition is a complex cognitive task. Recent data-informed approaches showed that children do not learn words uniformly at random but rather follow specific strategies based on the associative representation of words in the mental lexicon, a conceptual system enabling human cognitive computing. Building on this evidence, the current investigation introduces a combination of machine learning techniques, psycholinguistic features (i.e., frequency, length, polysemy and class) and multiplex lexical networks, representing the semantics and phonology of the mental lexicon, with the aim of predicting normative acquisition of 529 English words by toddlers between 22 and 26 months. Classifications using logistic regression and based on four psycholinguistic features achieve the best baseline cross-validated accuracy of 61.7% when half of the words have been acquired. Adding network information through multiplex closeness centrality enhances accuracy (up to 67.7%) more than adding multiplex neighbourhood density/degree (62.4%) or multiplex PageRank versatility (63.0%) or the best single-layer network metric, i.e., free association degree (65.2%), instead. Multiplex closeness operationalises the structural relevance of words for semantic and phonological information flow. These results indicate that the whole, global, multi-level flow of information and structure of the mental lexicon influence word acquisition more than single-layer or local network features of words when considered in conjunction with language norms. The highlighted synergy of multiplex lexical structure and psycholinguistic norms opens new ways for understanding human cognition and language processing through powerful and data-parsimonious cognitive computing approaches.
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11
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Zhou H, Zhang Y, Li J. An overlapping community detection algorithm in complex networks based on information theory. DATA KNOWL ENG 2018. [DOI: 10.1016/j.datak.2018.07.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Abstract
Symptoms of complex illnesses such as cancer often present with a high degree of heterogeneity between patients. At the same time, there are often core symptoms that act as common drivers for other symptoms, such as fatigue leading to depression and cognitive dysfunction. These symptoms are termed bridge symptoms and when combined with heterogeneity in symptom presentation, are difficult to detect using traditional unsupervised clustering techniques. This article develops a method for identifying patient communities based on bridge symptoms termed concordance network clustering. An empirical study of breast cancer symptomatology is presented, and demonstrates the applicability of this method for identifying bridge symptoms.
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Mishra B, Sun Y, Ahmed H, Liu X, Mukhtar MS. Global temporal dynamic landscape of pathogen-mediated subversion of Arabidopsis innate immunity. Sci Rep 2017; 7:7849. [PMID: 28798368 PMCID: PMC5552879 DOI: 10.1038/s41598-017-08073-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 06/29/2017] [Indexed: 12/22/2022] Open
Abstract
The universal nature of networks’ structural and physical properties across diverse systems offers a better prospect to elucidate the interplay between a system and its environment. In the last decade, several large-scale transcriptome and interactome studies were conducted to understand the complex and dynamic nature of interactions between Arabidopsis and its bacterial pathogen, Pseudomonas syringae pv. tomato DC3000. We took advantage of these publicly available datasets and performed “-omics”-based integrative, and network topology analyses to decipher the transcriptional and protein-protein interaction activities of effector targets. We demonstrated that effector targets exhibit shorter distance to differentially expressed genes (DEGs) and possess increased information centrality. Intriguingly, effector targets are differentially expressed in a sequential manner and make for 1% of the total DEGs at any time point of infection with virulent or defense-inducing DC3000 strains. We revealed that DC3000 significantly alters the expression levels of 71% effector targets and their downstream physical interacting proteins in Arabidopsis interactome. Our integrative “-omics”-–based analyses identified dynamic complexes associated with MTI and disease susceptibility. Finally, we discovered five novel plant defense players using a systems biology-fueled top-to-bottom approach and demonstrated immune-related functions for them, further validating the power and resolution of our network analyses.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, USA
| | - Yali Sun
- Department of Biology, University of Alabama at Birmingham, Birmingham, USA
| | - Hadia Ahmed
- Department of Computer & Information Sciences, University of Alabama at Birmingham, Birmingham, USA
| | - Xiaoyu Liu
- Department of Biology, University of Alabama at Birmingham, Birmingham, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, USA. .,Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, USA.
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15
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Sun PG, Ma X. Controllability and observability of cascading failure networks. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT 2017. [DOI: 10.1088/1742-5468/aa64f9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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16
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Sun PG, Ma X. Understanding the controllability of complex networks from the microcosmic to the macrocosmic. NEW JOURNAL OF PHYSICS 2017. [DOI: 10.1088/1367-2630/aa574f] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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17
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Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection. INFORMATION 2016. [DOI: 10.3390/info7010017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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19
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20
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Kianian S, Khayyambashi MR, Movahhedinia N. Semantic community detection using label propagation algorithm. J Inf Sci 2015. [DOI: 10.1177/0165551515592599] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The issue of detecting large communities in online social networks is the subject of a wide range of studies in order to explore the network sub-structure. Most of the existing studies are concerned with network topology with no emphasis on active communities among the large online social networks and social portals, which are not based on network topology like forums. Here, new semantic community detection is proposed by focusing on user attributes instead of network topology. In the proposed approach, a network of user activities is established and weighted through semantic data. Furthermore, consistent extended label propagation algorithm is presented. Doing so, semantic representations of active communities are refined and labelled with user-generated tags that are available in web.2. The results show that the proposed semantic algorithm is able to significantly improve the modularity compared with three previously proposed algorithms.
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Affiliation(s)
- Sahar Kianian
- Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
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21
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Sun PG, Gao L. A framework of mapping undirected to directed graphs for community detection. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2014.10.069] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Chaudhuri G, Clarke KC. On the Spatiotemporal Dynamics of the Coupling between Land Use and Road Networks: Does Political History Matter? ACTA ACUST UNITED AC 2015. [DOI: 10.1068/b39089] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The objective of this study was to investigate the impact of political history on the dynamics of the interrelationship between land use and road networks within cities. Political history, in this study, is defined as the combination of the regional-level government programs and political events that affect the pattern of urbanization in a region. The study focused on urbanization in the cities of Pordenone and Gorizia, both situated in the Friuli-Venezia Giulia region of northeastern Italy, over the period 1950–2000. Being located adjacent to an international boundary, the city of Gorizia has a long history of political instability dating back to the beginning of the 20th century. It is assumed that this political instability has led to the application of differential socioeconomic policies which have affected the process of urbanization in the city. The aim of the study was to capture this effect by investigating the structural changes over the period of fifty years in both land use and the road network. In order to understand the extent of the effect Pordenone was used for comparison, since it has experienced a relatively peaceful past and regular growth. MOLAND (monitoring land-use/cover dynamics) data for land use and the road network were used for the study. Graph theory measures were used for a comparative analysis of the structural properties of road networks in both cities and their development over time. In order to understand the spatial relationship between change in land use and the road network, a nonparametric test of the spatial correspondence of areal distribution was used and tested at multiple spatial scales. The results suggest that political history does affect the land-use and road-network changes individually, but it did not affect the type of spatial relationship that exists between the two for those particular cities. This research makes a unique attempt to analyze the impact of policy on land-use and road-network change by using spatial data and methods of analysis which can help to understand their overall dynamics and that can be used as an alternative to data-intensive and time-intensive simulation models.
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Affiliation(s)
- Gargi Chaudhuri
- Department of Geography and Earth Science, University of Wisconsin-La Crosse, 2022 Cowley Hall, La Crosse, WI 54601, USA
| | - Keith C Clarke
- Department of Geography, 1720 Ellison Hall, University of California Santa Barbara, Santa Barbara, CA 93106-4060, USA
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Lee JS, Hwang S, Yeo J, Kim D, Kahng B. Ground-state energy of the q-state Potts model: The minimum modularity. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 90:052140. [PMID: 25493772 DOI: 10.1103/physreve.90.052140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2014] [Indexed: 06/04/2023]
Abstract
A wide range of interacting systems can be described by complex networks. A common feature of such networks is that they consist of several communities or modules, the degree of which may quantified as the modularity. However, even a random uncorrelated network, which has no obvious modular structure, has a finite modularity due to the quenched disorder. For this reason, the modularity of a given network is meaningful only when it is compared with that of a randomized network with the same degree distribution. In this context, it is important to calculate the modularity of a random uncorrelated network with an arbitrary degree distribution. The modularity of a random network has been calculated [Reichardt and Bornholdt, Phys. Rev. E 76, 015102 (2007)PLEEE81539-375510.1103/PhysRevE.76.015102]; however, this was limited to the case whereby the network was assumed to have only two communities, and it is evident that the modularity should be calculated in general with q(≥2) communities. Here we calculate the modularity for q communities by evaluating the ground-state energy of the q-state Potts Hamiltonian, based on replica symmetric solutions assuming that the mean degree is large. We found that the modularity is proportional to 〈sqrt[k]〉/〈k〉 regardless of q and that only the coefficient depends on q. In particular, when the degree distribution follows a power law, the modularity is proportional to 〈k〉^{-1/2}. Our analytical results are confirmed by comparison with numerical simulations. Therefore, our results can be used as reference values for real-world networks.
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Affiliation(s)
- J S Lee
- School of Physics, Korea Institute for Advanced Study, Seoul 130-722, Republic of Korea
| | - S Hwang
- Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany and Department of Physics and Astronomy, Seoul National University, Seoul 151-747, Korea
| | - J Yeo
- School of Physics, Konkuk University, Seoul 143-701, Korea
| | - D Kim
- School of Physics, Korea Institute for Advanced Study, Seoul 130-722, Republic of Korea
| | - B Kahng
- Department of Physics and Astronomy, Seoul National University, Seoul 151-747, Korea
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Jia S, Gao L, Gao Y, Wang H. Anti-triangle centrality-based community detection in complex networks. IET Syst Biol 2014; 8:116-25. [PMID: 25014378 PMCID: PMC8687257 DOI: 10.1049/iet-syb.2013.0039] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Community detection has been extensively studied in the past decades largely because of the fact that community exists in various networks such as technological, social and biological networks. Most of the available algorithms, however, only focus on the properties of the vertices, ignoring the roles of the edges. To explore the roles of the edges in the networks for community discovery, the authors introduce the novel edge centrality based on its antitriangle property. To investigate how the edge centrality characterises the community structure, they develop an approach based on the edge antitriangle centrality with the isolated vertex handling strategy (EACH) for community detection. EACH first calculates the edge antitriangle centrality scores for all the edges of a given network and removes the edge with the highest score per iteration until the scores of the remaining edges are all zero. Furthermore, EACH is characterised by being free of the parameters and independent of any additional measures to determine the community structure. To demonstrate the effectiveness of EACH, they compare it with the state‐of‐the art algorithms on both the synthetic networks and the real world networks. The experimental results show that EACH is more accurate and has lower complexity in terms of community discovery and especially it can gain quite inherent and consistent communities with a maximal diameter of four jumps.
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Affiliation(s)
- Songwei Jia
- School of Computer Science and Technology, Xidian University, Xi'an 710071, People's Republic of China.
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, People's Republic of China
| | - Yong Gao
- Department of Computer Science, University of British Columbia Okanagan, Kelowna, British Columbia, Canada V1V 1V7, Canada
| | - Haiyang Wang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, People's Republic of China
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BELLO-ORGAZ GEMA, MENÉNDEZ HÉCTORD, CAMACHO DAVID. ADAPTIVE K-MEANS ALGORITHM FOR OVERLAPPED GRAPH CLUSTERING. Int J Neural Syst 2012; 22:1250018. [DOI: 10.1142/s0129065712500189] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.
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Affiliation(s)
- GEMA BELLO-ORGAZ
- Computer Science Department, Escuela Politecnica Superior, Universidad Autónoma de Madrid, 28049, Madrid, Spain
| | - HÉCTOR D. MENÉNDEZ
- Computer Science Department, Escuela Politecnica Superior, Universidad Autónoma de Madrid, 28049, Madrid, Spain
| | - DAVID CAMACHO
- Computer Science Department, Escuela Politecnica Superior, Universidad Autónoma de Madrid, 28049, Madrid, Spain
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Tsuji S, Ihara S, Aburatani H. A simple knowledge-based mining method for exploring hidden key molecules in a human biomolecular network. BMC SYSTEMS BIOLOGY 2012; 6:124. [PMID: 22979956 PMCID: PMC3740779 DOI: 10.1186/1752-0509-6-124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Accepted: 07/25/2012] [Indexed: 02/02/2023]
Abstract
BACKGROUND In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes. RESULTS To mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes. CONCLUSIONS The developed method, named NetHiKe (Network-based Hidden Key molecule miner), was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.
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Affiliation(s)
- Shingo Tsuji
- Genome Science Division, Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
- Komaba Open Laboratory, The University of Tokyo, Tokyo, Japan
| | - Sigeo Ihara
- Genome Science Division, Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
| | - Hiroyuki Aburatani
- Genome Science Division, Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
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Silva TC, Zhao L. Stochastic competitive learning in complex networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2012; 23:385-398. [PMID: 24808546 DOI: 10.1109/tnnls.2011.2181866] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning..
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Coscia M, Giannotti F, Pedreschi D. A classification for community discovery methods in complex networks. Stat Anal Data Min 2011. [DOI: 10.1002/sam.10133] [Citation(s) in RCA: 226] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Zanjani AAH, Darooneh AH. Finding communities in linear time by developing the seeds. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2011; 84:036109. [PMID: 22060458 DOI: 10.1103/physreve.84.036109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2010] [Revised: 05/09/2011] [Indexed: 05/31/2023]
Abstract
We present an alternate method for finding the communities in a complex network. We introduce two concepts named the seed of the community and the absorption power of the seed in complex networks. First, we find the seeds and then develop them by considering their absorption power to achieve the communities. We compare the modularity and the computational complexity of this algorithm with some other existing methods, and we show that this algorithm is very fast and efficient in comparison with some recently fast algorithms.
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Zio E, Sansavini G. Component criticality in failure cascade processes of network systems. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2011; 31:1196-1210. [PMID: 21371060 DOI: 10.1111/j.1539-6924.2011.01584.x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this work, specific indicators are used to characterize the criticality of components in a network system with respect to their contribution to failure cascade processes. A realistic-size network is considered as reference case study. Three different models of cascading failures are analyzed, differing both on the failure load distribution logic and on the cascade triggering event. The criticality indicators are compared to classical measures of topological centrality to identify the one most characteristic of the cascade processes considered.
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Papadopoulos S, Kompatsiaris Y, Vakali A, Spyridonos P. Community detection in Social Media. Data Min Knowl Discov 2011. [DOI: 10.1007/s10618-011-0224-z] [Citation(s) in RCA: 197] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Lai D, Lu H, Nardini C. Enhanced modularity-based community detection by random walk network preprocessing. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2010; 81:066118. [PMID: 20866489 DOI: 10.1103/physreve.81.066118] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2010] [Revised: 04/21/2010] [Indexed: 05/29/2023]
Abstract
The representation of real systems with network models is becoming increasingly common and critical to both capture and simplify systems' complexity, notably, via the partitioning of networks into communities. In this respect, the definition of modularity, a common and broadly used quality measure for networks partitioning, has induced a surge of efficient modularity-based community detection algorithms. However, recently, the optimization of modularity has been found to show a resolution limit, which reduces its effectiveness and range of applications. Therefore, one recent trend in this area of research has been related to the definition of novel quality functions, alternative to modularity. In this paper, however, instead of laying aside the important body of knowledge developed so far for modularity-based algorithms, we propose to use a strategy to preprocess networks before feeding them into modularity-based algorithms. This approach is based on the observation that dynamic processes triggered on vertices in the same community possess similar behavior patterns but dissimilar on vertices in different communities. Validations on real-world and synthetic networks demonstrate that network preprocessing can enhance the modularity-based community detection algorithms to find more natural clusters and effectively alleviates the problem of resolution limit.
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Affiliation(s)
- Darong Lai
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China.
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Chin A, Chignell M, Wang H. Tracking cohesive subgroups over time in inferred social networks. NEW REV HYPERMEDIA M 2010. [DOI: 10.1080/13614568.2010.496132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Mitrović M, Tadić B. Spectral and dynamical properties in classes of sparse networks with mesoscopic inhomogeneities. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2009; 80:026123. [PMID: 19792216 DOI: 10.1103/physreve.80.026123] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2008] [Revised: 03/04/2009] [Indexed: 05/28/2023]
Abstract
We study structure, eigenvalue spectra, and random-walk dynamics in a wide class of networks with subgraphs (modules) at mesoscopic scale. The networks are grown within the model with three parameters controlling the number of modules, their internal structure as scale-free and correlated subgraphs, and the topology of connecting network. Within the exhaustive spectral analysis for both the adjacency matrix and the normalized Laplacian matrix we identify the spectral properties, which characterize the mesoscopic structure of sparse cyclic graphs and trees. The minimally connected nodes, the clustering, and the average connectivity affect the central part of the spectrum. The number of distinct modules leads to an extra peak at the lower part of the Laplacian spectrum in cyclic graphs. Such a peak does not occur in the case of topologically distinct tree subgraphs connected on a tree whereas the associated eigenvectors remain localized on the subgraphs both in trees and cyclic graphs. We also find a characteristic pattern of periodic localization along the chains on the tree for the eigenvector components associated with the largest eigenvalue lambda(L)=2 of the Laplacian. Further differences between the cyclic modular graphs and trees are found by the statistics of random walks return times and hitting patterns at nodes on these graphs. The distribution of first-return times averaged over all nodes exhibits a stretched exponential tail with the exponent sigma approximately 1/3 for trees and sigma approximately 2/3 for cyclic graphs, which is independent of their mesoscopic and global structure.
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Affiliation(s)
- Marija Mitrović
- Scientific Computing Laboratory, Institute of Physics, 11000 Belgrade, Serbia
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Lü Z, Huang W. Iterated tabu search for identifying community structure in complex networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2009; 80:026130. [PMID: 19792223 DOI: 10.1103/physreve.80.026130] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2009] [Revised: 07/20/2009] [Indexed: 05/28/2023]
Abstract
This paper presents an iterated tabu search (denoted by ITS) algorithm for optimizing the modularity of community structure in complex networks. The proposed algorithm follows a general framework composed of two phases: basic optimization and postrefinement. When the basic optimization cannot improve the modularity any more, a postrefinement procedure is employed to further optimize the objective function with a global view. For both these two phases, iterated tabu search algorithm is employed to optimize the objective function. Computational results show the high effectiveness of the proposed ITS algorithm compared with six state-of-the-art algorithms in the literature. In particular, our ITS algorithm improves the previous best known modularity for several small and medium size networks.
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Affiliation(s)
- Zhipeng Lü
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
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Feldt S, Waddell J, Hetrick VL, Berke JD, Zochowski M. Functional clustering algorithm for the analysis of dynamic network data. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2009; 79:056104. [PMID: 19518518 PMCID: PMC2814878 DOI: 10.1103/physreve.79.056104] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2008] [Revised: 02/08/2009] [Indexed: 05/27/2023]
Abstract
We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.
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Affiliation(s)
- S Feldt
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.
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Yen L, Fouss F, Decaestecker C, Francq P, Saerens M. Graph nodes clustering with the sigmoid commute-time kernel: A comparative study. DATA KNOWL ENG 2009. [DOI: 10.1016/j.datak.2008.10.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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E W, Li T, Vanden-Eijnden E. Optimal partition and effective dynamics of complex networks. Proc Natl Acad Sci U S A 2008; 105:7907-12. [PMID: 18303119 PMCID: PMC2786939 DOI: 10.1073/pnas.0707563105] [Citation(s) in RCA: 77] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2007] [Indexed: 11/18/2022] Open
Abstract
Given a large and complex network, we would like to find the best partition of this network into a small number of clusters. This question has been addressed in many different ways. Here we propose a strategy along the lines of optimal prediction for the Markov chains associated with the dynamics on these networks. We develop the necessary ingredients for such an optimal partition strategy, and we compare our strategy with the previous ones. We show that when the Markov chain is lumpable, we recover the partition with respect to which the chain is lumpable. We also discuss the case of well-clustered networks. Finally, we illustrate our strategy on several examples.
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Affiliation(s)
- Weinan E
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544
- School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Tiejun Li
- Laboratory of Mathematics and Applied Mathematics and School of Mathematical Sciences, Peking University, Beijing 100871, China; and
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Cao L, Li X. Mixed evolutionary strategies imply coexisting opinions on networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2008; 77:016108. [PMID: 18351916 DOI: 10.1103/physreve.77.016108] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2007] [Revised: 10/22/2007] [Indexed: 05/26/2023]
Abstract
An evolutionary battle-of-the-sexes game is proposed to model the opinion formation on networks. The individuals of a network are partitioned into different classes according to their unaltered opinion preferences, and their factual opinions are considered as the evolutionary strategies, which are updated with the birth-death or death-birth rules to imitate the process of opinion formation. The individuals finally reach a consensus in the dominate opinion or fall into (quasi)stationary fractions of coexisting mixed opinions, presenting a phase transition at the critical modularity of the multiclass individuals' partitions on networks. The stability analysis on the coexistence of mixed strategies among multiclass individuals is given, and the analytical predictions agree well with the numerical simulations, indicating that the individuals of a community (or modular) structured network are prone to form coexisting opinions, and the coexistence of mixed evolutionary strategies implies the modularity of networks.
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Affiliation(s)
- Lang Cao
- Department of Automation, Shanghai Jiao Tong University Shanghai, 200240, China.
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Zhang S, Wang RS, Zhang XS. Uncovering fuzzy community structure in complex networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2007; 76:046103. [PMID: 17995056 DOI: 10.1103/physreve.76.046103] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2006] [Revised: 06/09/2007] [Indexed: 05/25/2023]
Abstract
There has been an increasing interest in properties of complex networks, such as small-world property, power-law degree distribution, and network transitivity which seem to be common to many real world networks. In this study, a useful community detection method based on non-negative matrix factorization (NMF) technique is presented. Based on a popular modular function, a proper feature matrix from diffusion kernel and NMF algorithm, the presented method can detect an appropriate number of fuzzy communities in which a node may belong to more than one community. The distinguished characteristic of the method is its capability of quantifying how much a node belongs to a community. The quantification provides an absolute membership degree for each node to each community which can be employed to uncover fuzzy community structure. The computational results of the method on artificial and real networks confirm its ability.
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Affiliation(s)
- Shihua Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China.
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Hellsten I, Lambiotte R, Scharnhorst A, Ausloos M. Self-citations, co-authorships and keywords: A new approach to scientists’ field mobility? Scientometrics 2007. [DOI: 10.1007/s11192-007-1680-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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