Detecting communities in networks by merging cliques

Author

Yan, Bowen ; Gregory, Steve

Author_Institution

Dept. of Comput. Sci., Univ. of Bristol, Bristol, UK

Volume

1

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

832

Lastpage

836

Abstract

Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks. One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges. Greedy approximate algorithms for maximizing modularity can be very fast and effective. We propose a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity. We show that this performs better than other similar algorithms in terms of both modularity and execution speed.

Keywords

data mining; greedy algorithms; disjoint cliques merging; disjoint community network detection; greedy algorithms; intercommunity edges; intracommunity edges; modularity optimization; Availability; Communication networks; Computer science; Data analysis; Data mining; Detection algorithms; Merging; Neural networks; Partitioning algorithms; Social network services; community structure; data mining; network analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5358036

Filename

5358036