CR-Graph: Community Reinforcement on Graphs for Accurate Community Detection
- Title
- CR-Graph: Community Reinforcement on Graphs for Accurate Community Detection
- Author
- 김상욱
- Keywords
- community detection; community reinforcement; inter-community edges; intra-community edges; preprocessing
- Issue Date
- 2020-10
- Publisher
- ACM CIKM 2020
- Citation
- Proceedings of the 29th ACM International Conference on Information & Knowledge Management, page. 2077-2080
- 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, CRGraph 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 CRGraph, we propose the following two strategies: (1) predicting intracommunity 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.
- URI
- https://dl.acm.org/doi/abs/10.1145/3340531.3412145?https://repository.hanyang.ac.kr/handle/20.500.11754/171309
- ISBN
- 978-1-4503-6859-9
- DOI
- 10.1145/3340531.3412145
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML