Efficient identification of node importance in social networks

Based on the theories of comprehensive decision-making, a multiattribute ranking method is presented.The proposed method is superior to other methods through contrast experiments.The theories of comprehensive decision-making are utilized to detect important nodes in social networks.This method can be used in different types and different scales of networks. In social networks, identifying influential nodes is essential to control the social networks. Identifying influential nodes has been among one of the most intensively studies of analyzing the structure of networks. There are a multitude of evaluation indicators of node importance in social networks, such as degree, betweenness and cumulative nomination and so on. But most of the indicators only reveal one characteristic of the node. In fact, in social networks, node importance is not affected by a single factor, but is affected by a number of factors. Therefore, the paper puts forward a relatively comprehensive and effective method of evaluation node importance in social networks by using the multi-objective decision method. Firstly, we select several different representative indicators given a certain weight. We regard each node as a solution and different indicators of each node as the solution properties. Then through calculating the closeness degree of each node to the ideal solution, we obtain evaluation indicator of node importance in social networks. Finally, we verify the effectiveness of the proposed method experimentally on a few actual social networks.

[1]  Panos M. Pardalos,et al.  Detecting critical nodes in sparse graphs , 2009, Comput. Oper. Res..

[2]  Marie-Claude Boily,et al.  Dynamical systems to define centrality in social networks , 2000, Soc. Networks.

[3]  Liu Zun,et al.  Key nodes in complex networks identified by multi-attribute decision-making method , 2013 .

[4]  Li De Mining Vital Nodes in Complex Networks , 2007 .

[5]  Ken A. Hawick,et al.  Node importance ranking and scaling properties of some complex road networks , 2007 .

[6]  P. Bonacich Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[7]  Rattikorn Hewett,et al.  Toward identification of key breakers in social cyber-physical networks , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[8]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[9]  Xiao-Long Ren,et al.  Review of ranking nodes in complex networks , 2014 .

[10]  M. Zelen,et al.  Rethinking centrality: Methods and examples☆ , 1989 .

[11]  Stephen P. Borgatti,et al.  Identifying sets of key players in a social network , 2006, Comput. Math. Organ. Theory.

[12]  Ming Tang,et al.  Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition , 2014, Scientific Reports.

[13]  Chen Hao and Wang Yitong,et al.  Threshold-Based Heuristic Algorithm for Influence Maximization , 2012 .

[14]  Richard Van Noorden Online collaboration: Scientists and the social network , 2014, Nature.

[15]  L. Freeman,et al.  Centrality in valued graphs: A measure of betweenness based on network flow , 1991 .

[16]  Luo Wan-bo Review on evaluation of node importance in public opinion , 2012 .

[17]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[18]  Wang Lin,et al.  Centralization of Complex Networks , 2006 .

[19]  Zhiqiang Zhang,et al.  A Joint Link Prediction Method for Social Network , 2015, ICYCSEE.

[20]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[21]  Guo Qiang,et al.  Analysis of the spreading influence of the nodes with minimum K-shell value in complex networks , 2013 .

[22]  P. Bonacich Factoring and weighting approaches to status scores and clique identification , 1972 .

[23]  Wang Bing-Hong,et al.  Node importance ranking of complex networks , 2013 .

[24]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[25]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[26]  J. A. Rodríguez-Velázquez,et al.  Subgraph centrality in complex networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Tibor Csendes,et al.  A local PageRank algorithm for evaluating the importance of scientific articles , 2015 .

[28]  Jun Hu,et al.  Evaluating Node Importance with Multi-Criteria , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[29]  Zheng Jing,et al.  Finding most vital node by node importance contribution matrix in communication netwoks , 2009 .

[30]  M. Small,et al.  Node importance for dynamical process on networks: a multiscale characterization. , 2011, Chaos.

[31]  Rudolf Ahlswede,et al.  Network information flow , 2000, IEEE Trans. Inf. Theory.

[32]  Gurminder Singh,et al.  Identifying key players in a social network: Measuring the extent of an individual's Neighbourhood Connectivity , 2010, 2010 IEEE International Workshop on: Business Applications of Social Network Analysis (BASNA).

[33]  Silvio Roberto Ignácio Pires,et al.  THE ROLE OF LOGISTICS SERVICES PROVIDERS IN THE SUPPLY CHAIN MANAGEMENT : THE SOCIAL NETWORK PERSPECTIVE , 2012 .

[34]  L. Freeman Centrality in social networks conceptual clarification , 1978 .