Learning cascaded influence under partial monitoring

Jie Zhang, Jiaqi Ma, Jie Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social influence has attracted tremendous attention from both academic and industrial communities due to the rapid development of online social networks. While most research has been focused on the direct influence between peers, learning cascaded indirect influence has not been previously studied. In this paper, we formulate the concept of cascade indirect influence based on the Independent Cascade model and then propose a novel online learning algorithm for learning the cascaded influence in the partial monitoring setting. We propose two bandit algorithms E-EXP3 and RE-EXP3 to address this problem. We theoretically prove that E-EXP3 has a cumulative regret bound of O(√T) over T, the number of time stamps. We will also show that RE-EXP3, a relaxed version of E-EXP3, achieves a better performance in practice. We compare the proposed algorithms with three baseline methods on both synthetic and real networks (Weibo and AMiner). Our experimental results show that RE-EXP3 converges 100× faster than E-EXP3. Both of them significantly outperform the alternative methods in terms of normalized regret. Finally, we apply the learned cascaded influence to help behavior prediction and experiments show that our proposed algorithms can help achieve a significant improvement (10-15% by accuracy) for behavior prediction.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages255-262
Number of pages8
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Externally publishedYes
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Publication series

NameProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/TerritoryUnited States
CitySan Francisco
Period8/18/168/21/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

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