INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

Abstract With the proliferation of graph applications in social network analysis, biological networks, WWW and many other areas, a great demand of efficient and scalable algorithms for graph mining is rising. In many applications, finding the most influential nodes in the network is informative for the network analyzers in order to track the spread of information, disease and rumors. The problem of finding the top k influential nodes of a directed graph G = ( V , E ) such that the influence spread of these nodes will be maximized has long been exposed and many algorithms have been proposed to deal with this problem. Despite the useful characteristics of community structure in social networks, only a few works have studied the role of communities in the spread of influence in social networks. In this paper we propose an efficient algorithm (which has an acceptable response time even for large graphs) for finding the influential nodes in the graph under linear threshold model. We exploit the community structure of graph to find the influential communities, and then find the influence of each node as a combination of its local and global influences. We compare our algorithm with the state-of-the-art methods for influence maximization problem and the results of our experiments on real world datasets show that our approach outperforms the other ones in the quality of outputted influential nodes while still has acceptable running time and memory usage for large graphs.

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

[2]  Jinha Kim,et al.  Scalable and parallelizable processing of influence maximization for large-scale social networks? , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[3]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[4]  Laks V. S. Lakshmanan,et al.  CELF++: optimizing the greedy algorithm for influence maximization in social networks , 2011, WWW.

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

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

[7]  Arunabha Sen,et al.  Influence propagation in adversarial setting: how to defeat competition with least amount of investment , 2012, CIKM.

[8]  Hoong Chuin Lau,et al.  Niche-seeking in influence maximization with adversary , 2012, ICEC '12.

[9]  Wei Chen,et al.  IMRank: influence maximization via finding self-consistent ranking , 2014, SIGIR.

[10]  Sangkeun Lee,et al.  Influence Maximization Algorithm Using Markov Clustering , 2013, DASFAA Workshops.

[11]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[12]  Laks V. S. Lakshmanan,et al.  SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model , 2011, 2011 IEEE 11th International Conference on Data Mining.

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

[14]  Sherman S. M. Chow,et al.  Secure Friend Discovery via Privacy-Preserving and Decentralized Community Detection , 2014, ArXiv.

[15]  Leslie G. Valiant,et al.  The Complexity of Enumeration and Reliability Problems , 1979, SIAM J. Comput..

[16]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  R. Luce,et al.  A method of matrix analysis of group structure , 1949, Psychometrika.

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

[19]  Yifei Yuan,et al.  Scalable Influence Maximization in Social Networks under the Linear Threshold Model , 2010, 2010 IEEE International Conference on Data Mining.

[20]  Ning Zhang,et al.  Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process , 2012, AAAI.

[21]  Donald B. Johnson,et al.  Finding All the Elementary Circuits of a Directed Graph , 1975, SIAM J. Comput..

[22]  Chuan Zhou,et al.  Personalized influence maximization on social networks , 2013, CIKM.

[23]  Xiaoming Liu,et al.  SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[24]  D. Kroft,et al.  All paths through a maze , 1967 .

[25]  Wei Chen,et al.  Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model , 2011, SDM.