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A Graph-Cut-Based Approach to Community Detection in Networksopen access

Authors
Shin, HyungsikPark, JeryangKang, 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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