论文信息 - Discovering Most Influential Social Networks Nodes Based on Floating Influence

Discovering Most Influential Social Networks Nodes Based on Floating Influence

Social network influence maximization problem aims to design algorithms that can maximize the scope of the nodes affected by the specified nodes set. This paper studies and improves existing algorithms by the introduction of hidden influence and floating influence. Experiments show that the HGA algorithm works more effectively.

Chuan Li | Wei Zeng | Qian Luo | Ning Yang | Bingqing Feng

[1] Wei Chen,et al. Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[2] Jeffrey C. Mogul. Network Locality at the Scale of Processes , 1992, ACM Trans. Comput. Syst..

[3] Pablo A. Estévez,et al. Selecting the Most Influential Nodes in Social Networks , 2007, 2007 International Joint Conference on Neural Networks.

[4] M E J Newman,et al. Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5] Matthew Richardson,et al. Mining the network value of customers , 2001, KDD '01.

[6] Xiaolong Jin,et al. Exploring social influence via posterior effect of word-of-mouth recommendations , 2012, WSDM '12.

[7] Jia-Tang Tian,et al. A New Hybrid Algorithm for Influence Maximization in Social Networks: A New Hybrid Algorithm for Influence Maximization in Social Networks , 2011 .

[8] Jon Kleinberg,et al. Maximizing the spread of influence through a social network , 2003, KDD '03.

[9] Andreas Krause,et al. Cost-effective outbreak detection in networks , 2007, KDD '07.

[10] Jimeng Sun,et al. Social influence analysis in large-scale networks , 2009, KDD.

[11] Yu Wang,et al. Community-based greedy algorithm for mining top-K influential nodes in mobile social networks , 2010, KDD.