Computer Science ›› 2016, Vol. 43 ›› Issue (Z6): 404-409.doi: 10.11896/j.issn.1002-137X.2016.6A.096

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Event-based Node Influence Analysis in Social Network Evolution

XIONG Chao, CHEN Yun-fang and CANG Ji-yun   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Social influence analysis is an important research focus in the field of social network research,and most exis-ting works on influence analysis focus on static network.This paper proposed a method of influence analysis based on individual events for network evolution.The traditional diffusion model was improved to adapt the evolution,and the events that exhibited in the diffusion was defined.Then two indicators based on the individual events which are social index and influence index were given to measure the influence of node.The value of the two indicators of nodes were analyzed separately in the experiment to find out the important influential node in the influence maximization problem.Then the performance of the two indicators was compared.The results show that the nodes of social index have higher efficiency than nodes of influence index at the initial diffusion stage,but when the diffusion meets the bottleneck,nodes of influence index can break the bottleneck faster so that they can infect more nodes in the network.

Key words: Network evolution,Network events,Influence diffusion,Influence maximization

[1] Goldenberg J,Libai B,Muller E.Using complex systems analysis to advance marketing theory development:Modeling heterogeneity effects on new product growth through stochastic cellular automata[J].Academy of Marketing Science Review,2001,9(3):1-18
[2] Goldenberg J,Libai B,Muller E.Talk of the network:A complex systems look at the underlying process of word-of-mouth[J].Marketing Letters,2001,12(3):211-223
[3] Ma H,Yang H,Lyu M R,et al.Mining social networks using heat diffusion processes for marketing candidates selection[C]∥Proceedings of the 17th ACM Conference on Information and Knowledge Management.ACM,2008:233-242
[4] Richardson M,Domingos P.Mining knowledge-sharing sites for viral marketing[C]∥Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2002:61-70
[5] Kempe D,Kleinberg J,Tardos é.Maximizing the spread of influ-ence through a social network[C]∥Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2003:137-146
[6] Galstyan A,Musoyan V,Cohen P.Maximizing influence propagation in networks with community structure[J].Physical Review E,2009,79(5):056102
[7] Cao T,Wu X,Wang S,et al.OASNET:an optimal allocation approach to influence maximization in modular social networks[C]∥Proceedings of the 2010 ACM Symposium on Applied Computing.ACM,2010:1088-1094
[8] Asur S,Parthasarathy S,Ucar D.An event-based framework for characterizing the evolutionary behavior of interaction graphs[J].ACM Transactions on Knowledge Discovery from Data (TKDD),2009,3(4):913-921
[9] Berger-Wolf T Y,Saia J.A framework for analysis of dynamic social networks[C]∥Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.ACM,2006:523-528
[10] Habiba,Yu Y,Berger-Wolf T Y,et al.Finding spread blockers in dynamic networks[M]∥Advances in Social Network Mining and Analysis.Springer Berlin Heidelberg,2010:55-76
[11] Zhuang H,Sun Y,Tang J,et al.Influence maximization in dynamic social networks[C]∥2013 IEEE 13th International Conference on Data Mining (ICDM).IEEE,2013:1313-1318
[12] Wu Bin,Wang Bai,Yang Sheng-qi.the evolution of the socialnetwork analytical framework based on events [J].Journal of Software,2011(7):1488-1502
[13] Ilhan N,Oguducu I G.Community Event Prediction in Dynamic Social Networks[C]∥2013 12th International Conference on Machine Learning and Applications (ICMLA).IEEE,2013:191-196

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