A Graph-Cut-Based Approach to Community Detection in Networksopen access
- Authors
- Shin, Hyungsik; Park, Jeryang; Kang, Dongwoo
- Issue Date
- 2-Jun-2022
- Publisher
- MDPI
- Keywords
- community detection; graph cut; betweenness centrality; modularity
- Citation
- APPLIED SCIENCES-BASEL, v.12, no.12
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 12
- Number
- 12
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32075
- DOI
- 10.3390/app12126218
- ISSN
- 2076-3417
2076-3417
- Abstract
- Networks can be used to model various aspects of our lives as well as relations among many real-world entities and objects. To detect a community structure in a network can enhance our understanding of the characteristics, properties, and inner workings of the network. Therefore, there has been significant research on detecting and evaluating community structures in networks. Many fields, including social sciences, biology, engineering, computer science, and applied mathematics, have developed various methods for analyzing and detecting community structures in networks. In this paper, a new community detection algorithm, which repeats the process of dividing a community into two smaller communities by finding a minimum cut, is proposed. The proposed algorithm is applied to some example network data and shows fairly good community detection results with comparable modularity Q values.
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- Appears in
Collections - College of Engineering > School of Electronic & Electrical Engineering > 1. Journal Articles
- College of Engineering > Civil and Environmental Engineering > Journal Articles
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