A local multiresolution algorithm for detecting communities of unbalanced structures

Krista Rizman Žalik and Borut Žalik

Physica A: Statistical Mechanics and its Applications, 2014, vol. 407, issue C, 380-393

Abstract: In complex networks such as computer and information networks, social networks or biological networks a community structure is a common and important property. Community detection in complex networks has attracted a lot of attention in recent years. Community detection is the problem of finding closely related groups within a network. Modularity optimisation is a widely accepted method for community detection. It has been shown that the modularity optimisation has a resolution limit because it is unable to detect communities with sizes smaller than a certain number of vertices defined with network size. In this paper we propose a metric for describing community structures that enables community detection better than other metrics. We present a fast local expansion algorithm for community detection. The proposed algorithm provides online multiresolution community detection from a source vertex. Experimental results show that the proposed algorithm is efficient in both real-world and synthetic networks.

Keywords: Modularity; Objective function; Community detection; Dense subgraphs; Networks (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:407:y:2014:i:c:p:380-393

DOI: 10.1016/j.physa.2014.03.059

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