Density shrinking algorithm for community detection with path based similarity

Jianshe Wu, Yunting Hou, Yang Jiao, Yong Li, Xiaoxiao Li and Licheng Jiao

Physica A: Statistical Mechanics and its Applications, 2015, vol. 433, issue C, 218-228

Abstract: Community structure is ubiquitous in real world complex networks. Finding the communities is the key to understand the functions of those networks. A lot of works have been done in designing algorithms for community detection, but it remains a challenge in the field. Traditional modularity optimization suffers from the resolution limit problem. Recent researches show that combining the density based technique with the modularity optimization can overcome the resolution limit and an efficient algorithm named DenShrink was provided. The main procedure of DenShrink is repeatedly finding and merging micro-communities (broad sense) into super nodes until they cannot merge. Analyses in this paper show that if the procedure is replaced by finding and merging only dense pairs, both of the detection accuracy and runtime can be obviously improved. Thus an improved density-based algorithm: ImDS is provided. Since the time complexity, path based similarity indexes are difficult to be applied in community detection for high performance. In this paper, the path based Katz index is simplified and used in the ImDS algorithm.

Keywords: Complex network; Density shrinking; Network clustering; Community detection (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:433:y:2015:i:c:p:218-228

DOI: 10.1016/j.physa.2015.03.044

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