The bi-directional h-index and B-core decomposition in directed networks

Li Zhai, Xiangbin Yan and Guojing Zhang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 531, issue C

Abstract: Identifying important nodes is crucial for understanding network structure and function in the research of network science. A variety of methods are proposed to evaluate different quality aspects of node’s importance, in most cases ignoring the directed nature of links. In this paper, we introduce the bi-directional h-index to measure node’s importance, and the bi-directional k-core (B-core) decomposition for partition of network and detection dense subgraphs in directed networks. Considering the direction of links in directed networks, the bi-directional h-index and B-core decomposition can reflect the mutual influence of two kinds of reverse relations. By B-core decomposition, each node is assigned a bi-directional coreness to express its importance. The bi-directional h-index uses h-index algorithm for iterative calculation, and the directed degree centrality is its initial value, we prove that bi-directional coreness is its stable value. When the network is undirected, the bi-directional h-index is equivalent to the node’s h-index, and the B-core decomposition is equivalent to the classic graph-theoretic notion of k-core decomposition. Finally, we show the computation and convergence of bi-directional h-index and its difference from other methods in ranking of node’s importance in a real-world directed network.

Keywords: Directed network; H-index; B-core decomposition; Node centrality; Partition of network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119309574

DOI: 10.1016/j.physa.2019.121715

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