Title
A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks.
Abstract
A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.
Year
DOI
Venue
2019
10.1109/ACCESS.2018.2884447
IEEE ACCESS
Keywords
Field
DocType
Overlapping community detection,complex network,weighted network,dense subgraph
Vertex (geometry),Computer science,Network topology,Robustness (computer science),Theoretical computer science,Weighted network,Complex network,Optimization algorithm,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Qi Li192.13
Jiang Zhong2579.19
Qing Li33222433.87
Chen Wang420.69
Ze-Hong Cao59615.40