A similarity-based community detection method with multiple prototype representation

Kuang Zhou, Arnaud Martin and Quan Pan

Physica A: Statistical Mechanics and its Applications, 2015, vol. 438, issue C, 519-531

Abstract: Communities are of great importance for understanding graph structures in social networks. Some existing community detection algorithms use a single prototype to represent each group. In real applications, this may not adequately model the different types of communities and hence limits the clustering performance on social networks. To address this problem, a Similarity-based Multi-Prototype (SMP) community detection approach is proposed in this paper. In SMP, vertices in each community carry various weights to describe their degree of representativeness. This mechanism enables each community to be represented by more than one node. The centrality of nodes is used to calculate prototype weights, while similarity is utilized to guide us to partitioning the graph. Experimental results on computer generated and real-world networks clearly show that SMP performs well for detecting communities. Moreover, the method could provide richer information for the inner structure of the detected communities with the help of prototype weights compared with the existing community detection models.

Keywords: Multiple prototype; Node similarity; Community detection; Prototype weights (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:438:y:2015:i:c:p:519-531

DOI: 10.1016/j.physa.2015.07.016

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