Title of Paper:Community detection by enhancing community structure in bipartite networks

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First Author:Zhou, Wenjie

Correspondence Author:Wang, XY (reprint author), Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China.; Wang, XY (reprint author), Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China.

Co-author:Wang, Xingyuan,Zhang, Chuan,Li, Rui,Wang, Chunpeng

Date of Publication:2019-03-10

Journal:MODERN PHYSICS LETTERS B

Included Journals:SCIE

Volume:33

Issue:7

ISSN No.:0217-9849

Key Words:Bipartite networks; network topology; community structure; overlapping node

Abstract:Community detection is one of the primary tools to discover useful information that is hidden in complex networks. Some community detection algorithms for bipartite networks have been proposed from various viewpoints. However, the performance of these algorithms deteriorates when the community structure becomes unclear. Enhancing community structure remains a nontrivial task. In this paper, we propose a community detection algorithm, called ECD, that enhances community structure in bipartite networks. In the proposed ECD, the topology of a network is modified by reducing unnecessary edges that are connected to neighboring low-weight communities. Therefore, an ambiguous community structure is converted into a structure that is much clearer than the original structure. The experimental results on both artificial and real-world networks verify the accuracy and reliability of our algorithm. Compared with existing community detection algorithms using state-of-the-art methods, our algorithm has better performance.