Advanced Graph Clustering Methods: A Comprehensive and In-Depth Analysis
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 55 references that are in the map. First posted on arXiv 12 Jul 2024.
Builds on
20 of 55 references.
References · 20
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2016Variational Graph Auto-Encoderscited by 1,373
- 2019Contrastive Multiview Codingcited by 1,297
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
- 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
- 2020Contrastive Multi-View Representation Learning on Graphscited by 651
- 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
- 2018Deep Learning on Graphs: A Surveycited by 393
- 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
8 more
- 2019Self-Supervised Learning by Cross-Modal Audio-Video Clusteringcited by 264
- 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
- 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
- 2019Linkage Based Face Clustering via Graph Convolution Networkcited by 55
- 2020A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecastingcited by 54
- 2019A Review of Stochastic Block Models and Extensions for Graph Clusteringcited by 46
- 2020Deep Learning for Community Detection: Progress, Challenges and Opportunitiescited by 42
- 2022DeepCut: Unsupervised Segmentation using Graph Neural Networks Clusteringcited by 25
Built on by
Citing papers per year: 2025: 1.
1 paper.