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International Conference on Database Systems for Advanced Applications

DASFAA 2020: Database Systems for Advanced Applications pp 120–136Cite as

STIM: Scalable Time-Sensitive Influence Maximization in Large Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12114))

Abstract

Influence maximization, aiming to select k seed users to influence the rest of users maximally, is a fundamental problem in social networks. Due to its well-known NP-hardness, great efforts have been devoted to developing scalable algorithms in the literature. However, the scalability issue is still not well solved in the time-sensitive influence maximization problem when propagation incurs a certain amount of time delay and only be valid before a deadline constraint, because all possible time delays need to be enumerated along each edge in a path to calculate the influence probability. Existing approaches usually adopt a path-based search strategy to enumerate all the possible influence spreading paths for a single path, which are computationally expensive for large social networks. In this paper, we propose a novel scalable time-sensitive influence maximization method, STIM, based on time-based search that can avoid a large number of repeated visits of the same subpaths and compute the influence probability more efficiently. Furthermore, based on time-based search, we also derive a new upper bound to estimate the marginal influence spread efficiently. Extensive experiments on real-world networks show that STIM is more space and time-efficient compared with existing state-of-the-art methods while still preserving the influence spread quality in real-world large social networks.

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Notes

  1. 1.

    http://snap.stanford.edu/data.

  2. 2.

    https://www.aminer.cn/cosnet.

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Acknowledgment

The work was supported in part by grants of Natural Science Foundation 61972291, Natural Science Foundation of Hubei Province 2018CFB519 and 2018CFB616, Fundamental Research Funds for the Central Universities 413000078, The Key program of New Generation Information Technology Innovation of the Ministry of Education 2019J01011.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Yuanyuan Zhu, Kailin Ding & Ming Zhong

  2. Economics and Management School, Wuhan University, Wuhan, China

    Lijia Wei

Authors
  1. Yuanyuan Zhu
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  2. Kailin Ding
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  3. Ming Zhong
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  4. Lijia Wei
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Corresponding author

Correspondence to Yuanyuan Zhu .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of Systems Engineering and En, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Steven Euijong Whang

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Zhu, Y., Ding, K., Zhong, M., Wei, L. (2020). STIM: Scalable Time-Sensitive Influence Maximization in Large Social Networks. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-59419-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59418-3

  • Online ISBN: 978-3-030-59419-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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