教師資料查詢 | 類別: 期刊論文 | 教師: 陳以錚 YI-CHENG CHEN (瀏覽個人網頁)

標題:CIM: Community-Based Influence Maximization in Social Networks
學年102
學期2
出版(發表)日期2014/04/01
作品名稱CIM: Community-Based Influence Maximization in Social Networks
作品名稱(其他語言)
著者Chen, Yi-Cheng; Peng, Wen-Chih; Lee, Wan-Chien; Lee, Suh-Yin
單位淡江大學資訊工程學系
出版者New York: Association for Computing Machinery, Inc.
著錄名稱、卷期、頁數ACM Transactions on Intelligent Systems and Technology 5(2), Article 25, pp.1-31
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.
關鍵字Community detection;diffusion models;influence maximization;social network analysis
語言英文(美國)
ISSN2157-6904
期刊性質國外
收錄於SCI;EI;
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式,紙本
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