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Algaba E, Prieto A, Saavedra-Nieves A. Risk analysis sampling methods in terrorist networks based on the Banzhaf value. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:477-492. [PMID: 37210375 DOI: 10.1111/risa.14156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
This article introduces the Banzhaf and the Banzhaf-Owen values as novel measures of risk analysis of a terrorist attack, determining the most dangerous terrorists in a network. This new approach counts with the advantage of integrating at the same time the complete topology (i.e., nodes and edges) of the network and a coalitional structure on the nodes of the network. More precisely, the characteristics of the nodes (e.g., terrorists) of the network and their possible relationships (e.g., types of communication links), as well as coalitional information (e.g., level of hierarchies) independent of the network. First, for these two new measures of risk analysis, we provide and implement approximation algorithms. Second, as illustration, we rank the members of the Zerkani network, responsible for the attacks in Paris (2015) and Brussels (2016). Finally, we give a comparison between the rankings established by the Banzhaf and the Banzhaf-Owen values as measures of risk analysis.
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Affiliation(s)
- Encarnación Algaba
- Departamento de Matemática Aplicada II, Escuela Superior de Ingeniería de la Universidad de Sevilla, Sevilla, Spain
- Instituto de Matemáticas de la Universidad de Sevilla (IMUS), Universidad de Sevilla, Sevilla, Spain
| | - Andrea Prieto
- Departamento de Matemática Aplicada II, Escuela Superior de Ingeniería de la Universidad de Sevilla, Sevilla, Spain
| | - Alejandro Saavedra-Nieves
- Departamento de Estatística, Análise Matemática e Optimización, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
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2
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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.
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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
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Yu JZ, Wu M, Bichler G, Aros-Vera F, Gao J. Reconstructing Sparse Multiplex Networks with Application to Covert Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:142. [PMID: 36673283 PMCID: PMC9857694 DOI: 10.3390/e25010142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/07/2023] [Accepted: 01/08/2023] [Indexed: 06/17/2023]
Abstract
Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation-Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.
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Affiliation(s)
- Jin-Zhu Yu
- Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
| | - Mincheng Wu
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310058, China
| | - Gisela Bichler
- School of Criminology and Criminal Justice, California State University, San Bernardino, CA 92407, USA
| | - Felipe Aros-Vera
- Department of Industrial and Systems Engineering, Ohio University, Athens, OH 45701, USA
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY 12180, USA
- Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY 12180, USA
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4
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Knowledge graph embedding by projection and rotation on hyperplanes for link prediction. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03983-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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5
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MSGWO-MKL-SVM: A Missing Link Prediction Method for UAV Swarm Network Based on Time Series. MATHEMATICS 2022. [DOI: 10.3390/math10142535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average.
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The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks. FUTURE INTERNET 2022. [DOI: 10.3390/fi14050123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Traditional social network analysis can be generalized to model some networked systems by multilayer structures where the individual nodes develop relationships in multiple layers. A multilayer network is called multiplex if each layer shares at least one node with some other layer. In this paper, we built a unique criminal multiplex network from the pre-trial detention order by the Preliminary Investigation Judge of the Court of Messina (Sicily) issued at the end of the Montagna anti-mafia operation in 2007. Montagna focused on two families who infiltrated several economic activities through a cartel of entrepreneurs close to the Sicilian Mafia. Our network possesses three layers which share 20 nodes. The first captures meetings between suspected criminals, the second records phone calls and the third detects crimes committed by pairs of individuals. We used measures from multilayer network analysis to characterize the actors in the network based on their local edges and their relevance to each specific layer. Then, we used measures of layer similarity to study the relationships between different layers. By studying the actor connectivity and the layer correlation, we demonstrated that a complete picture of the structure and the activities of a criminal organization can be obtained only considering the three layers as a whole multilayer network and not as single-layer networks. Specifically, we showed the usefulness of the multilayer approach by bringing out the importance of actors that does not emerge by studying the three layers separately.
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Characterization of Group Behavior of Corruption in Construction Projects Based on Contagion Mechanism. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8456197. [PMID: 35345798 PMCID: PMC8957412 DOI: 10.1155/2022/8456197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/09/2022] [Accepted: 02/19/2022] [Indexed: 11/17/2022]
Abstract
With the rapid development of construction projects, more and more engineering corruption problems have emerged. Therefore, this paper proposes a SEIR (susceptible-exposed-infected-recovered) based corruption model to better understand the propagation process of corruption cases in construction projects. In this model, the data samples are collected from the 2018 Engineering Corruption Case Judgment Document, the propagation parameters are obtained through actual case analysis with the help of complex networks, the change process and key influencing factors of actual nodes in engineering corruption cases are simulated by Python. The study results indicate that the personnel conforms to the “4–9 transmission law,” in which the early stage is a period of high incidence of corruption cases. The network of corruption cases is somewhat vulnerable, and its spread is about minus 8 times the change in crackdown rate and 10 times the change in infection rate. The variation range of the susceptible population S and the removed person R in the propagation simulation curve can predict the relationship between corruption infection rate and crackdown rate, which can provide theoretical guidance for preventing the occurrence of corruption.
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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.
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Affiliation(s)
- Tao Zhou
- CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
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9
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Improving link prediction in social networks using local and global features: a clustering-based approach. PROGRESS IN ARTIFICIAL INTELLIGENCE 2021. [DOI: 10.1007/s13748-021-00261-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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10
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Ficara A, Cavallaro L, Curreri F, Fiumara G, De Meo P, Bagdasar O, Song W, Liotta A. Criminal networks analysis in missing data scenarios through graph distances. PLoS One 2021; 16:e0255067. [PMID: 34379625 PMCID: PMC8357088 DOI: 10.1371/journal.pone.0255067] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 07/08/2021] [Indexed: 12/05/2022] Open
Abstract
Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.
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Affiliation(s)
- Annamaria Ficara
- DMI Department, University of Palermo, Palermo, Italy
- MIFT Department, University of Messina, Messina, Italy
| | - Lucia Cavallaro
- School of Computing and Engineering, University of Derby, Derby, United Kingdom
| | - Francesco Curreri
- DMI Department, University of Palermo, Palermo, Italy
- MIFT Department, University of Messina, Messina, Italy
| | | | | | - Ovidiu Bagdasar
- School of Computing and Engineering, University of Derby, Derby, United Kingdom
| | - Wei Song
- College of Information Technology, Shanghai Ocean University, Shanghai, China
| | - Antonio Liotta
- Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy
- * E-mail:
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11
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Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks. INFORMATION 2021. [DOI: 10.3390/info12050214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.
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12
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Xian X, Wu T, Liu Y, Wang W, Wang C, Xu G, Xiao Y. Towards link inference attack against network structure perturbation. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106674] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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13
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Min S, Gao Z, Peng J, Wang L, Qin K, Fang B. STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106746] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries. SUSTAINABILITY 2021. [DOI: 10.3390/su13031251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As the raw material of lithium-ion batteries, lithium carbonate plays an important role in the development of new energy field. Due to the extremely uneven distribution of lithium resources in the world, the security of supply in countries with less say would be greatly threatened if trade restrictions or other accidents occurred in large-scale exporting countries. It is of great significance to help these countries find new partners based on the existing trade topology. This study uses the link prediction method, based on the perspective of the topological structure of trade networks in various countries and trade rules, and eliminates the influence of large-scale lithium carbonate exporting countries on the lithium carbonate trade of other countries, to find potential lithium carbonate trade links among importing and small-scale exporting countries, and summarizes three trade rules: (1) in potential relationships involving two net importers, a relationship involving either China or the Netherlands is more likely to occur; (2) for all potential relationships, a relationship that actually occurred for more than two years in the period in 2009–2018 is more likely to occur in the future; and (3) potential relationships pairing a net exporter with a net importer are more likely to occur than other country combinations. The results show that over the next five to six years, Denmark and Italy, Netherlands and South Africa, Turkey and USA are most likely to have a lithium carbonate trading relationship, while Slovenia and USA, and Belgium and Thailand are the least likely to trade lithium carbonate. Through this study, we can strengthen the supply security of lithium carbonate resources in international trade, and provide international trade policy recommendations for the governments of importing countries and small-scale exporting countries.
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Wang XW, Chen Y, Liu YY. Link Prediction through Deep Generative Model. iScience 2020; 23:101626. [PMID: 33103070 PMCID: PMC7575873 DOI: 10.1016/j.isci.2020.101626] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/19/2020] [Accepted: 09/24/2020] [Indexed: 02/04/2023] Open
Abstract
Inferring missing links based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine, e-commerce, social media, and criminal intelligence. Numerous methods have been proposed to solve the link prediction problem. Yet, many of these methods are designed for undirected networks only and based on domain-specific heuristics. Here we developed a new link prediction method based on deep generative models, which does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities. When applied to various real-world networks from different domains, our method shows overall superior performance against existing methods. A novel link prediction method based on deep generative models is developed This method works for general undirected or directed complex networks Leveraging structural patterns at different scales, this method outperforms others
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Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Yize Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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16
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Graph regularization weighted nonnegative matrix factorization for link prediction in weighted complex network. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.068] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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17
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Colliri T, Zhao L. Analyzing the Bills-Voting Dynamics and Predicting Corruption-Convictions Among Brazilian Congressmen Through Temporal Networks. Sci Rep 2019; 9:16754. [PMID: 31728035 PMCID: PMC6856083 DOI: 10.1038/s41598-019-53252-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/24/2019] [Indexed: 11/11/2022] Open
Abstract
In this paper, we propose a network-based technique to analyze bills-voting data comprising the votes of Brazilian congressmen for a period of 28 years. The voting sessions are initially mapped into static networks, where each node represents a congressman and each edge stands for the similarity of votes between a pair of congressmen. Afterwards, the constructed static networks are converted to temporal networks. Our analyses on the temporal networks capture some of the main political changes happened in Brazil during the period of time under consideration. Moreover, we find out that the bills-voting networks can be used to identify convicted politicians, who commit corruption or other financial crimes. Therefore, we propose two conviction prediction methods, one is based on the highest weighted convicted neighbor and the other is based on link prediction techniques. It is a surprise to us that the high accuracy (up to 90% by the link prediction method) on predicting convictions is achieved only through bills-voting data, without taking into account any financial information beforehand. Such a feature makes possible to monitor congressmen just by considering their legal public activities. In this way, our work contributes to the large scale public data study using complex networks.
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Affiliation(s)
- Tiago Colliri
- Dept. of Computer Science, ICMC-USP, Sao Carlos, Brazil.
| | - Liang Zhao
- Dept. of Computing and Mathematics, FFCLRP-USP, Ribeirao Preto, Brazil
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18
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Using network motifs to characterize temporal network evolution leading to diffusion inhibition. SOCIAL NETWORK ANALYSIS AND MINING 2019. [DOI: 10.1007/s13278-019-0556-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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19
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Wang W, Tang M, Jiao P. A unified framework for link prediction based on non-negative matrix factorization with coupling multivariate information. PLoS One 2018; 13:e0208185. [PMID: 30496261 PMCID: PMC6264521 DOI: 10.1371/journal.pone.0208185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 11/13/2018] [Indexed: 11/18/2022] Open
Abstract
Many link prediction methods have been developed to infer unobserved links or predict missing links based on the observed network structure that is always incomplete and subject to interfering noise. Thus, the performance of existing methods is usually limited in that their computation depends only on input graph structures, and they do not consider external information. The effects of social influence and homophily suggest that both network structure and node attribute information should help to resolve the task of link prediction. This work proposes SASNMF, a link prediction unified framework based on non-negative matrix factorization that considers not only graph structure but also the internal and external auxiliary information, which refers to both the node attributes and the structural latent feature information extracted from the network. Furthermore, three different combinations of internal and external information are proposed and input into the framework to solve the link prediction problem. Extensive experimental results on thirteen real networks, five node attribute networks and eight non-attribute networks show that the proposed framework has competitive performance compared with benchmark methods and state-of-the-art methods, indicating the superiority of the presented algorithm.
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Affiliation(s)
- Wenjun Wang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Minghu Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
- School of Computer Science and Technology, Qinghai Nationalities University, Qinghai, China
- * E-mail:
| | - Pengfei Jiao
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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20
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Extension of neighbor-based link prediction methods for directed, weighted and temporal social networks. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.06.051] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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