Identifying key nodes based on improved structural holes in complex networks

Hui Yu, Xi Cao, Zun Liu and Yongjun Li

Physica A: Statistical Mechanics and its Applications, 2017, vol. 486, issue C, 318-327

Abstract: Identifying key nodes in complex networks is of theoretical and practical significance. Local metrics such as degree centrality is simplest but cannot effectively identify the important bridging nodes. Global metrics such as betweenness and closeness centrality can better identify important nodes, but they are often restricted by the unknown topology and cannot be conveniently applied in large-scale networks. In this paper, we propose an effective ranking method based on an Improved Structural Holes (ISH) to identify the important nodes. ISH method only uses the degree of nodes and the nearest neighborhood information rather than considering the global structure of a network. Our experimental results on five complex networks show that the proposed method can effectively identify the key nodes in complex networks and can also be applied in large-scale or unconnected networks.

Keywords: Complex networks; Key nodes; Structural holes; SIR model (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:486:y:2017:i:c:p:318-327

DOI: 10.1016/j.physa.2017.05.028

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