Journal
KNOWLEDGE-BASED SYSTEMS
Volume 195, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2020.105626
Keywords
Complex network; Community detection; Link prediction; Central node
Categories
Funding
- Key Project of Science and Technology Innovation 2030 by Ministry of Science and Technology of China [2018AAA0101300]
- National Natural Science Foundation of China [61672033, 61822301, 61876123, U1804262]
Ask authors/readers for more resources
Community detection in complex networks has aroused wide attention, since it can find some useful information hidden in the networks. Many different community detection algorithms have been proposed to detect the communities in a variety of networks. However, as the ratio of each node connecting with the nodes in other communities increases, namely, the community structure of networks becomes unclear, the performance of most existing community detection algorithms will considerately deteriorate. As a method of finding missing information, link prediction can predict undiscovered edges in the networks. However, the existing link prediction based community detection algorithms cannot deal with the networks with an ambiguous community structure, namely, the networks having a mixing parameter greater than 0.5. In this paper, we design a new strategy of link prediction and propose a community detection algorithm based on this strategy to detect the communities in complex networks, especially for the networks with an ambiguous community structure. Experimental results on synthetic benchmark networks and real-world networks indicate that the proposed community detection algorithm outperforms five state-of-the-art community detection algorithms, especially for those without a clear community structure. (C) 2020 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available