Efficient algorithms for influence maximization in social networks

Yi Cheng Chen*, Wen-Chih Peng, Suh Yin Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.

Original languageEnglish
Pages (from-to)577-601
Number of pages25
JournalKnowledge and Information Systems
Issue number3
StatePublished - 19 Nov 2012


  • Community discovery
  • Diffusion models
  • Influence maximization
  • Social network

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