A Comprehensive Review of Community Detection in Graphs
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866911953066131456 |
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| author | Li, Jiakang Lai, Songning Shuai, Zhihao Tan, Yuan Jia, Yifan Yu, Mianyang Song, Zichen Peng, Xiaokang Xu, Ziyang Ni, Yongxin Qiu, Haifeng Yang, Jiayu Liu, Yutong Lu, Yonggang |
| author_facet | Li, Jiakang Lai, Songning Shuai, Zhihao Tan, Yuan Jia, Yifan Yu, Mianyang Song, Zichen Peng, Xiaokang Xu, Ziyang Ni, Yongxin Qiu, Haifeng Yang, Jiayu Liu, Yutong Lu, Yonggang |
| contents | The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11798 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Review of Community Detection in Graphs Li, Jiakang Lai, Songning Shuai, Zhihao Tan, Yuan Jia, Yifan Yu, Mianyang Song, Zichen Peng, Xiaokang Xu, Ziyang Ni, Yongxin Qiu, Haifeng Yang, Jiayu Liu, Yutong Lu, Yonggang Social and Information Networks Machine Learning The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. |
| title | A Comprehensive Review of Community Detection in Graphs |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2309.11798 |