CIM: Community-based influence maximization in social networks

Yi Cheng Chen, Wen Yuan Zhu, Wen Chih Peng, Wang Chien Lee, Suh Yin Lee

Research output: Contribution to journalArticlepeer-review

143 Scopus citations


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.

Original languageEnglish (US)
Article number25
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
StatePublished - Apr 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence


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