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H-core decomposition for directed networks and its application

Author

Listed:
  • Xiaoyu Chen

    (Shaanxi Normal University
    Zhejiang Normal University)

  • Yang Liu

    (Zhejiang Normal University
    Yili Normal University)

  • Zhenxin Cao

    (Zhejiang Normal University)

  • Xiaopeng Li

    (Northwest A&F University)

  • Jinde Cao

    (Southeast University
    Ahlia University)

Abstract

In this paper, we introduce a directed weighted h-index and a bi-directional h-core decomposition for directed networks, aimed at better identifying important nodes and dense subgraphs. This directed weighted h-index combines the edges’ direction and weight in a directed network, and it can effectively measure the centrality of nodes. To obtain the h-core, we design an iterative algorithm, and we develop a bi-directional h-core decomposition method for partitioning the nodes in a network. As an application, we apply the directed weighted h-index and algorithm to the CEL neural network, USAir network and Social network to identify dense subgraphs and important nodes. Comparative analysis with existing h-type indices demonstrates that our proposed directed weighted h-index is a superior measure of centrality in terms of its ability to identify important nodes and dense subgraphs more accurately.

Suggested Citation

  • Xiaoyu Chen & Yang Liu & Zhenxin Cao & Xiaopeng Li & Jinde Cao, 2024. "H-core decomposition for directed networks and its application," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6571-6596, November.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05170-5
    DOI: 10.1007/s11192-024-05170-5
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