CR-Graph: Community Reinforcement for Accurate Community Detection
- Authors
- Kang, Yoonsuk; Lee, Jun Seok; Shin, Won-Yong; Kim, Sang-Wook
- Issue Date
- Oct-2020
- Publisher
- Association for Computing Machinery
- Keywords
- community detection; community reinforcement; inter-community edges; intra-community edges; preprocessing
- Citation
- International Conference on Information and Knowledge Management, Proceedings, pp.2077 - 2080
- Indexed
- SCOPUS
- Journal Title
- International Conference on Information and Knowledge Management, Proceedings
- Start Page
- 2077
- End Page
- 2080
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144955
- DOI
- 10.1145/3340531.3412145
- Abstract
- In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently.
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