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HM-EIICT: Fairness-aware link prediction in complex networks using community information

Author

Listed:
  • Akrati Saxena

    (Eindhoven University of Technology)

  • George Fletcher

    (Eindhoven University of Technology)

  • Mykola Pechenizkiy

    (Eindhoven University of Technology)

Abstract

The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

Suggested Citation

  • Akrati Saxena & George Fletcher & Mykola Pechenizkiy, 2022. "HM-EIICT: Fairness-aware link prediction in complex networks using community information," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2853-2870, November.
    • Handle: RePEc:spr:jcomop:v:44:y:2022:i:4:d:10.1007_s10878-021-00788-0
    DOI: 10.1007/s10878-021-00788-0
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517.
    3. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    4. Wu, Zhihao & Lin, Youfang & Wang, Jing & Gregory, Steve, 2016. "Link prediction with node clustering coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 1-8.
    5. Liu, Shuxin & Ji, Xinsheng & Liu, Caixia & Bai, Yi, 2017. "Extended resource allocation index for link prediction of complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 174-183.
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