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A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks

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Abstract

The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot spot. Although conventional centrality-based and greedy-based methods show high performance, they are not very efficient when dealing with large-scale social networks. Recently, algorithms based on swarm intelligence are applied to solve the problems mentioned above, and the existing researches show that such algorithms can obtain the optimal global solution. In particular, the discrete bat algorithm (DBA) has been proved to have excellent performance, but the evolution mechanism based on a random selection strategy leads to the optimal solution's instability. To solve this problem, in this paper, we propose a clique-DBA algorithm. The proposed algorithm is based on the clique partition of a network and enhances the initial DBA algorithm's stability. The experimental results show that the proposed clique-DBA algorithm converges to a determined local influence estimation (LIE) value in each run, eliminating the phenomenon of large fluctuation of LIE fitness value generated by the original DBA algorithm. Finally, the simulated results achieved under the independent cascade model show that the clique-DBA algorithm has a comparable performance of influence spreading compared with the algorithms proposed in the state of the art.

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Acknowledgements

This work was partially supported by the National Key R&D Program of China under Grant No. 2020YFC0832500, Ministry of Education—China Mobile Research Foundation under Grant No. MCM20170206, The Fundamental Research Funds for the Central Universities under Grant No. lzujbky-2019-kb51 and lzujbky-2018-k12, National Natural Science Foundation of China under Grant No. 61402210, Major National Project of High-Resolution Earth Observation System under Grant No. 30-Y20A34-9010-15/17, State Grid Corporation of China Science and Technology Project under Grant No. SGGSKY00WYJS2000062, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, Strategic Priority Research Program of the Chinese Academy of Sciences with Grant No. XDA03030100, Google Research Awards, and Google Faculty Award, Science and Technology Plan of Qinghai Province under Grant No. 2020-GX-164, National Social Science Fund Project under Grant No.20XTJ005, National Social Science Fund Project under Grant No.18BTJ001 and Zhejiang Provincial Natural Science Foundation under Grant No. LQ20F020011. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Jetson TX1 used for this research.

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Lihong Han & Qingguo Zhou

  2. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  3. Department of Computer Science, University of Salerno, Fisciano, SA, Italy

    Arcangelo Castiglione

  4. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China

    Jianxin Tang

  5. School of Statistics, Lanzhou University of Finance and Economics, Lanzhou, China

    Lihong Han & Hengjun Huang

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  1. Lihong Han
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  2. Kuan-Ching Li
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  3. Arcangelo Castiglione
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  4. Jianxin Tang
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  5. Hengjun Huang
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  6. Qingguo Zhou
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Correspondence to Qingguo Zhou.

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Han, L., Li, KC., Castiglione, A. et al. A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks. Soft Comput 25, 8223–8240 (2021). https://doi.org/10.1007/s00500-021-05749-7

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