Title
Overlapping Community Detection Of Bipartite Networks Based On A Novel Community Density
Abstract
Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure's merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network's modularity.
Year
DOI
Venue
2021
10.3390/fi13040089
FUTURE INTERNET
Keywords
DocType
Volume
overlapping community, bipartite network, community detection, community density
Journal
13
Issue
Citations 
PageRank 
4
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Yubo Peng100.34
Bofeng Zhang2103.86
Furong Chang301.69