Title
An Overlapping Community Detection Approach In Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization
Abstract
Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.
Year
DOI
Venue
2021
10.3390/sym13050869
SYMMETRY-BASEL
Keywords
DocType
Volume
overlapping community detection, ego-splitting network, nonnegative matrix factorization, graph symmetry theory, priori information embedding
Journal
13
Issue
Citations 
PageRank 
5
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Mingqing Huang100.34
Qingshan Jiang258877.27
qiang qu38312.15
Abdur Rasool401.35