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Influence Maximization in Independent Cascade Model with Limited Propagation Distance

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Web Technologies and Applications (APWeb 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8710))

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Abstract

Influence Maximization (IM) is the problem of finding k most influential users in a social network. In this paper, a novel propagation model named Independent Cascade Model with Limited Propagation Distance (ICLPD) is established. In the ICLPD, the influence of seed nodes can only propagate limited hops and the transmission capacities of the seed nodes are different. It is proved that IM problem in the ICLPD is NP-hard and the influence spread function has submodularity. Thus a greedy algorithm can be used to get a result which guarantees a ratio of (1 − 1/e) approximation. In addition, an efficient heuristic algorithm named Local Influence Discount Heuristic (LIDH) is proposed to speed up the greedy algorithm. Extensive experiments on two real-world datasets show LIDH works well in the ICLPD. LIDH is several orders of magnitude faster than the greedy algorithm while its influence spread is close to that of the greedy algorithm.

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Author information

Authors and Affiliations

  1. National Engineering Laboratory for Information Content Analysis Technology, Department of Electronic Engineering, Shanghai Jiao Tong University, China

    Shunming Lv & Li Pan

Authors
  1. Shunming Lv
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  2. Li Pan
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Editor information

Editors and Affiliations

  1. School of Computer Science, National University of Defense Technology, 410073, Changsha, China

    Weihong Han

  2. School of Information Technology & Electrical Engineering, University of Queensland, 4107, Brisbane, QLD, Australia

    Zi Huang

  3. School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China

    Changjun Hu

  4. School of Computer Science and Technology, Harbin Institute of Technology, 150006, Harbin, China

    Hongli Zhang

  5. Institute of Information Engineering, Chinese Academy of Sciences, 100864, Beijing, China

    Li Guo

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© 2014 Springer International Publishing Switzerland

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Lv, S., Pan, L. (2014). Influence Maximization in Independent Cascade Model with Limited Propagation Distance. In: Han, W., Huang, Z., Hu, C., Zhang, H., Guo, L. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8710. Springer, Cham. https://doi.org/10.1007/978-3-319-11119-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-11119-3_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11118-6

  • Online ISBN: 978-3-319-11119-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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