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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.09055 |
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| _version_ | 1866910524324708352 |
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| author | Watteau, Timothé Bonnefoy, Aubin Illouz-Laurent, Simon Jusseau, Joaquim Iovleff, Serge |
| author_facet | Watteau, Timothé Bonnefoy, Aubin Illouz-Laurent, Simon Jusseau, Joaquim Iovleff, Serge |
| contents | Graph clustering, which aims to divide a graph into several homogeneous groups, is a critical area of study with applications that span various fields such as social network analysis, bioinformatics, and image segmentation. This paper explores both traditional and more recent approaches to graph clustering. Firstly, key concepts and definitions in graph theory are introduced. The background section covers essential topics, including graph Laplacians and the integration of Deep Learning in graph analysis. The paper then delves into traditional clustering methods, including Spectral Clustering and the Leiden algorithm. Following this, state-of-the-art clustering techniques that leverage deep learning are examined. A comprehensive comparison of these methods is made through experiments. The paper concludes with a discussion of the practical applications of graph clustering and potential future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced Graph Clustering Methods: A Comprehensive and In-Depth Analysis Watteau, Timothé Bonnefoy, Aubin Illouz-Laurent, Simon Jusseau, Joaquim Iovleff, Serge Machine Learning Graph clustering, which aims to divide a graph into several homogeneous groups, is a critical area of study with applications that span various fields such as social network analysis, bioinformatics, and image segmentation. This paper explores both traditional and more recent approaches to graph clustering. Firstly, key concepts and definitions in graph theory are introduced. The background section covers essential topics, including graph Laplacians and the integration of Deep Learning in graph analysis. The paper then delves into traditional clustering methods, including Spectral Clustering and the Leiden algorithm. Following this, state-of-the-art clustering techniques that leverage deep learning are examined. A comprehensive comparison of these methods is made through experiments. The paper concludes with a discussion of the practical applications of graph clustering and potential future research directions. |
| title | Advanced Graph Clustering Methods: A Comprehensive and In-Depth Analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.09055 |