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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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