Detection of core-periphery structure in networks based on 3-tuple motifs

Chuang Ma, Bing Bing Xiang, Han Shuang Chen, Michael Small, Hai Feng Zhang

Research output: Contribution to journalArticle

3 Citations (Scopus)
371 Downloads (Pure)

Abstract

Detecting mesoscale structure, such as community structure, is of vital importance for analyzing complex networks. Recently, a new mesoscale structure, core-periphery (CP) structure, has been identified in many real-world systems. In this paper, we propose an effective algorithm for detecting CP structure based on a 3-tuple motif. In this algorithm, we first define a 3-tuple motif in terms of the patterns of edges as well as the property of nodes, and then a motif adjacency matrix is constructed based on the 3-tuple motif. Finally, the problem is converted to find a cluster that minimizes the smallest motif conductance. Our algorithm works well in different CP structures: including single or multiple CP structure, and local or global CP structures. Results on the synthetic and the empirical networks validate the high performance of our method.

Original languageEnglish
Article number053121
JournalChaos
Volume28
Issue number5
DOIs
Publication statusPublished - 1 May 2018

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