Community-based influence maximization for viral marketing

  • Huimin Huang
  • Hong ShenEmail author
  • Zaiqiao Meng
  • Huajian Chang
  • Huaiwen He


Derived from the idea of word-to-mouth advertising and with applying information diffusion theory, viral marketing attracts wide research interests because of its business value. As an effective marketing strategy, viral marketing is to select a small set of initial users based on trust among close social circles of friends or families so as to maximize the spread of influence in the social network. In this paper, we propose a new community-based influence maximization method for viral marketing that integrates community detection into influence diffusion modeling, instead of performing community detection independently, to improve the performance. We first build a comprehensive latent variable model which captures community-level topic interest, item-topic relevance and community membership distribution of each user, and we propose a collapsed Gibbs sampling algorithm to train the model. Then we infer community-to-community influence strength using topic-irrelevant influence and community topic interest, and further infer user-to-user influence strength using community-to-community influence strength and community membership distribution of each user. Finally we propose a community-based heuristic algorithm to mine influential nodes that selects the influential nodes with a divide-and-conquer strategy, considering both topic-aware and community-relevant to enhance quality and improve efficiency. Extensive experiments are conducted to evaluate effectiveness and efficiency of our proposals. The results validate our ideas and show the superiority of our method compared with state-of-the-art influence maximization algorithms.


Social networks Viral marketing Influence maximization Latent variable model 



This work is supported by National Key R & D Program of China Project #2017YFB0203201 and Australian Research Council Discovery Project DP150104871.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Huimin Huang
    • 1
  • Hong Shen
    • 1
    • 2
  • Zaiqiao Meng
    • 1
  • Huajian Chang
    • 1
  • Huaiwen He
    • 1
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.School of Computer ScienceUniversity of AdelaideAdelaideAustralia

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