Community detection in complex networks using structural similarity

Fataneh Dabaghi Zarandi and Marjan Kuchaki Rafsanjani

Physica A: Statistical Mechanics and its Applications, 2018, vol. 503, issue C, 882-891

Abstract: These days, community detection is an important field to understand the topology and functions in the complex networks. In this article, we propose a novel Community Detection Algorithm based on Structural Similarity (CDASS) that executed in two consecutive phases. In the first phase, we randomly remove some low similarity edges. Therefore, the network graph is converted into several disconnected components that are considered as primary communities. In the following, the primary communities are merged in order to identify the final community structure close to real communities. In the second phase, we use an our identified evaluation function to select the best communities between overall random generated partitions. Finally, we evaluate CDASS algorithm using several scenarios extracted from artificial and real networks. The results, obtained from simulation with these scenarios, show that proposed algorithm detects communities with high accuracy close to optimal case and is applicable in the large and small network topologies.

Keywords: Community detection; Complex networks; Structural similarity; Modularity (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437118303066
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:503:y:2018:i:c:p:882-891

DOI: 10.1016/j.physa.2018.02.212

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().