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International Conference on Data Science

ICDS 2015: Data Science pp 6–15Cite as

Minimizing the Social Influence from a Topic Modeling Perspective

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9208))

Abstract

In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking k nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.

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Acknowledgements

This work was supported by the 973 project (No. 2013CB 329606), and the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences (No. XDA06030200), Australia ARC Discovery Project (DP1402206).

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Qipeng Yao & Li Guo

  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qipeng Yao

Authors
  1. Qipeng Yao
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  2. Li Guo
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Corresponding author

Correspondence to Qipeng Yao .

Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Xi'an Jiaotong University, Xi'an Jiaotong, China

    Wei Huang

  3. Chinese Academy of Sciences, Beijing, China

    Yong Shi

  4. University of Illinois Chicago, CHICAGO, Illinois, USA

    Philip S. Yu

  5. Fudan University, Shanghai, China

    Yangyong Zhu

  6. Research Center on Fictitious Econo, Chinese Academy of Sciences, Beijing, China

    Yingjie Tian

  7. University of Technology, Sydney, Australia

    Peng Zhang

  8. Victoria University, Melbourne, Victoria, Australia

    Jing He

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© 2015 Springer International Publishing Switzerland

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Yao, Q., Guo, L. (2015). Minimizing the Social Influence from a Topic Modeling Perspective. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-24474-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24473-0

  • Online ISBN: 978-3-319-24474-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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