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A New Community-based Algorithm for Influence Maximization in Social Network ⋆

By Jiaguo Lv, Jingfeng Guo and Huixiao Ren

Abstract

Influence maximization is a fundamental issue to find a set of key individuals in social network such that targeting them initially will maximize the spread of influence. However, the problem of finding the most key nodes is NP-hard. It is shown that a greedy algorithm with provable approximation guarantees can give good approximation. However, it is too computationally expensive to apply in a large social network. Based on the community structure of social network, a cooperative game theoretic algorithm (CGINA) to find key nodes is proposed. In CGINA, we first detect the community structure of the social network with the topological structure and information diffusion model. Then, we will find key nodes in communities. Different from other literature, we think of the information diffusion in the whole network as a cooperative game with transferable utility. The communities of the network happen to be the players in the game. With the Shapley value in game theory, we allocate the number of key nodes for each community. In my view, the key nodes include two parts. One is composed of “bridge ” nodes, which are easy to propagate information across communities, the other is composed of “influential” nodes, which can diffuse information quickly in its own community. Empirical studies on a large social network show that our algorithm is efficient and powerful

Topics: Cooperation Game, Influence Maximization, Shapley Value, CGINA
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.2616
Provided by: CiteSeerX
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