Conference Paper

ArnetMiner: extraction and mining of academic social networks

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DOI: 10.1145/1401890.1402008 Conference: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Source: DBLP
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    ABSTRACT: Motivated by viral marketing, the problem of influence maximization in social networks has attracted much attention in recent years and several studies have been done on that. However, almost all of these studies are focused on the progressive influence models, such as independent cascade (IC) and Linear threshold (LT) models, which cannot capture the reversibility in actions. In this paper, we present the Heat Conduction (HC) model which is a non-progressive influence model and has favorable real world interpretations. We also show that HC unifies, generalizes, and extends the existing nonprogressive models, such as Voter model and non-progressive LT [1]. In addition, we tackle the influence maximization problem for HC, which is proved to be NP-hard, with a scalable and provably near-optimal solution; we prove that the influence spread is submodular under HC and apply the greedy method. To the best of our knowledge, we are the first to present a scalable solution for influence maximization under non-progressive LT model, as a special case of HC model. Our fast and efficient algorithm benefits from two key properties of the proposed HC framework, where we establish closed-form expressions for the influence function computation and the greedy seed selection. Through extensive experiments on several real and synthetic networks, we validate the efficacy of our algorithm and demonstrate that it outperforms the state-of-the-art methods in terms of both influence spread and scalability.
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May 16, 2014