期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 519, 期 -, 页码 181-194出版社
ELSEVIER
DOI: 10.1016/j.physa.2018.12.023
关键词
Complex network; Community detection; Edge-deleting; Restriction; Modularity
资金
- Hunan Provincial Natural Science Foundation of China [2015JJ2032]
- Fund for Promoting the reform of higher education by using big data technology, Energizing teachers and students to explore the future [2017A01067]
Community detection is an important task with great practical significance for understanding the structure and function of complex networks in various fields. As the real world networks become larger and more complex, it is a challenge to achieve high quality of community partitioning. In order to identify community structure more effectively in complex networks, a new algorithm, which iteratively deletes edges with restrictions is proposed in this paper. The algorithm first makes use of the connection strength between vertices to divide the original network into some strongly connected communities with optimal modularity by the improved edge-deleting process, and finally reconnects the isolated vertices to initial communities for optimizing community structure. Experiments on the real-world and synthetic networks prove that the proposed algorithm achieves a competitive performance compared with other reference algorithms. (C) 2018 Elsevier B.V. All rights reserved.
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