Influence Maximizing and Local Influenced Community Detection Based on Multiple Spread Model

In independent cascade model, an active node has only one chance to activate its neighbors, while in reality an active node has many chances to activate its neighbors. We propose an influence diffusion model called multiple spread model, in which an active node has many activation chances. We prove that influence maximizing problem with the proposed model is submodular and monotone, which means greedy algorithm provides (1-1/e) approximation to optimal solution. However, computation time costs much due to Monte Carlo simulation in greedy algorithm. We propose a two-phase method which leverages community information to find seeds. In order to evaluate influence of a particular node, we also propose a definition of local influenced community as well as an algorithm called LICD to detect local influenced community. Experiments show that the proposed model and algorithms are both efficient and effective in problems of influence maximizing and local influenced community detection.

[1]  Inderjit S. Dhillon,et al.  Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.

[2]  Feng Luo,et al.  Exploring Local Community Structures in Large Networks , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[3]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[4]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[5]  Johannes Fürnkranz,et al.  Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings , 2006, PKDD.

[6]  Karl Branting,et al.  Incremental Detection of Local Community Structure , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[7]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[8]  A. Clauset.  Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Randy Goebel,et al.  Local Community Identification in Social Networks , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[10]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

[13]  Y. Narahari,et al.  A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks , 2011, IEEE Transactions on Automation Science and Engineering.

[14]  Yifei Yuan,et al.  Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate , 2011, SDM.

[15]  James P. Bagrow.  Evaluating local community methods in networks , 2007, 0706.3880.

[16]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[17]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[18]  Masahiro Kimura,et al.  Tractable Models for Information Diffusion in Social Networks , 2006, PKDD.

[19]  Michael R. Lyu,et al.  Mining social networks using heat diffusion processes for marketing candidates selection , 2008, CIKM '08.