A Pagerank-Inspired Heuristic Scheme for Influence Maximization in Social Networks

Bo Zhang, Yufeng Wang, Qun Jin and Jianhua Ma
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Bo Zhang: Nanjing University of Posts and Telecommunications, Nanjing, China
Yufeng Wang: Nanjing University of Posts and Telecommunications, Nanjing, China
Qun Jin: China Jiliang University, Hangzhou, China & Waseda University, Tokyo, Japan
Jianhua Ma: Hosei University, Tokyo, Japan

International Journal of Web Services Research (IJWSR), 2015, vol. 12, issue 4, 48-62

Abstract: This article focused on seeking a new heuristic algorithm for the influence maximization problem in complex social networks, in which a small subset of individuals are intentionally selected as seeds to trigger a large cascade of further adoptions of a new behavior under certain influence cascade models. In literature, degree and other centrality-based heuristics are commonly used to estimate the influential power of individuals in social networks. The major issues with degree-based heuristics are twofold. First, those results are only derived for the uniform IC model, in which propagation probabilities on all social links are set as same, which is rarely the case in reality; Second, intuitively, an individual's influence power depends not only on the number of direct friends, but also relates to kinds of those friends, that is, the neighbors' influence should also be taken into account when measuring one's influential power. Based on the general weighted cascade model (WC), this article proposes Pagerank-inspired heuristic scheme, PRDiscount, which explicitly discounts the influence power of those individuals who have social relationships with chosen seeds, to alleviate the “overlapping effect” occurred in behavior diffusion. Then, the authors use both the artificially constructed social network graphs (with the features of power-law degree distribution and small-world characteristics) and the real-data traces of social networks to verify the performance of their proposal. Simulations illustrate that PRDiscount can advantage over the existing degree-based discount algorithm, DegreeDiscount, and achieve the comparable performance as greedy algorithm.

Date: 2015
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