A local multiresolution algorithm for detecting communities of unbalanced structures

Citation

Žalik, Krista R. & Žalik, Borut (2014). A local multiresolution algorithm for detecting communities of unbalanced structures. Physica A: Statistical Mechanics and its Applications. vol. 407 pp. 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.

URL

http://dx.doi.org/10.1016/j.physa.2014.03.059

Keyword(s)

Modularity Communities Dense subgraphs Networks

Reference Type

Journal Article

Journal Title

Physica A: Statistical Mechanics and its Applications

Author(s)

Žalik, Krista R.
Žalik, Borut

Year Published

2014

Volume Number

407

Pages

380-393

DOI

10.1016/j.physa.2014.03.059

Reference ID

7698