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    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98125

    題名: CIM: Community-Based Influence Maximization in Social Networks
    作者: Chen, Yi-Cheng;Peng, Wen-Chih;Lee, Wan-Chien;Lee, Suh-Yin
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Community detection;diffusion models;influence maximization;social network analysis
    日期: 2014-04-01
    上傳時間: 2014-05-27 09:15:46 (UTC+8)
    出版者: A C M Special Interest Group
    摘要: Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
    關聯: ACM Transactions on Intelligent Systems and Technology 5(2), Article 25, pp.1-31
    DOI: 10.1145/2532549
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


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