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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 255-262
Jie Zhang , Department of Physics, Tsinghua University
Jiaqi Ma , Department of Automation, Tsinghua University
Jie Tang , Department of Computer Science, Tsinghua University
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.
INDEX TERMS
Monitoring, Social network services, Prediction algorithms, Games, Integrated circuit modeling, Heuristic algorithms
CITATION

J. Zhang, J. Ma and J. Tang, "Learning cascaded influence under partial monitoring," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 255-262.
doi:10.1109/ASONAM.2016.7752243
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