H-core decomposition for directed networks and its application

Xiaoyu Chen (), Yang Liu (), Zhenxin Cao (), Xiaopeng Li () and Jinde Cao ()
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Xiaoyu Chen: Shaanxi Normal University
Yang Liu: Zhejiang Normal University
Zhenxin Cao: Zhejiang Normal University
Xiaopeng Li: Northwest A&F University
Jinde Cao: Southeast University

Scientometrics, 2024, vol. 129, issue 11, No 4, 6596 pages

Abstract: 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.

Keywords: Directed weightedh-index; Identification of important nodes; Directed strength; H-core decomposition; Division of networks; Directed network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-024-05170-5

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