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Influence Maximization Revisited

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Databases Theory and Applications (ADC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14386))

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Abstract

Influence Maximization (IM) has been extensively studied, which is to select a set of k seed users from a social network to maximize the expected number of influenced users in the social network. There are many approaches proposed under a cascade model to find such a single set of k seed users. Such a set being computed may not be unique, as it is most likely that there exist more than one set, \(S_1, S_2, \cdots \), each of them leads to the same IM, given a social network exhibits rich symmetry as reported in the literature. In this paper, first, we study how to select a set of k seed users from a set of seed \(k'~(\ge k)\) users which can be either a union of sets of seed users, \(\mathbb {S} = \bigcup _i S_i\), where \(S_i\) is a set of k seed users, or simply a set of seed users of size \(k'~(\ge k)\) being computed, based on cooperative game using Shapley value. Second, we develope a visualization system to explore the process of influence spreading from topological perspective, as IM only gives the expected number of influenced users without much information on how influence spreads in a large social network. We conduct experimental studies to confirm the effectiveness of the seed users selected in our approach.

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Acknowledgement

This work is supported by the Research Grants Council of Hong Kong, No. 14202919 and No. 14205520.

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR

    Yihan Geng, Kunyu Wang, Ziqi Liu, Michael Yu & Jeffrey Xu Yu

Authors
  1. Yihan Geng
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  2. Kunyu Wang
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  3. Ziqi Liu
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  4. Michael Yu
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Corresponding author

Correspondence to Michael Yu .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zhifeng Bao

  2. The University of Melbourne, Melbourne, VIC, Australia

    Renata Borovica-Gajic

  3. The University of Queensland, Brisbane, QLD, Australia

    Ruihong Qiu

  4. The University of Melbourne, Melbourne, VIC, Australia

    Farhana Choudhury

  5. The University of New South Wales, Sydney, NSW, Australia

    Zhengyi Yang

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Geng, Y., Wang, K., Liu, Z., Yu, M., Yu, J.X. (2024). Influence Maximization Revisited. In: Bao, Z., Borovica-Gajic, R., Qiu, R., Choudhury, F., Yang, Z. (eds) Databases Theory and Applications. ADC 2023. Lecture Notes in Computer Science, vol 14386. Springer, Cham. https://doi.org/10.1007/978-3-031-47843-7_25

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  • DOI: https://doi.org/10.1007/978-3-031-47843-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47842-0

  • Online ISBN: 978-3-031-47843-7

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