Detecting community structure in networks via consensus dynamics and spatial transformation

Bo Yang, He He and Xiaoming Hu

Physica A: Statistical Mechanics and its Applications, 2017, vol. 483, issue C, 156-170

Abstract: We present a novel clustering algorithm for community detection, based on the dynamics towards consensus and spatial transformation. The community detection problem is translated to a clustering problem in the N-dimensional Euclidean space by three stages: (1) the dynamics running on a network is emulated to a procedure of gas diffusion in a finite space; (2) the pressure distribution vectors are used to describe the influence that each node exerts on the whole network; (3) the similarity measures between two nodes are quantified in the N-dimensional Euclidean space by k-Nearest Neighbors method. After such steps, we could merge clusters according to their similarity distances and show the community structure of a network by a hierarchical clustering tree. Tests on several benchmark networks are presented and the results show the effectiveness and reliability of our algorithm.

Keywords: Networks; Community detection; Consensus; Clustering; Spatial transformation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:483:y:2017:i:c:p:156-170

DOI: 10.1016/j.physa.2017.04.098

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