Structural Influence Maximization in Social Networks
2019 INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA)(2019)
摘要
With the development and widespread of social applications, large social networks has appeared. Influence maximization problem over social networks has become a popular topic and caused lots of research interests. Given a social network with influence weights on edges and an integer k, the goal of influence maximization problem is to find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Previous information propagation models do not consider the effects of structural information on influences, but only consider the influences between nodes independently. In this paper, a novel definition of information propagation model is proposed and the related influence maximization problems is studied. Theoretical analysis shows that the problem is NP-complete, heuristic algorithms are proposed to solve the influence maximization problem on the new model. Finally, the experimental results show that the algorithm proposed is efficient and effective.
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关键词
structural influence, influence maximization, NP-hard, heuristic algorithms
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