Welcome to the Awesome Multi-View Graph Clustering repository! This is a curated collection of resources, papers, and methodologies dedicated to Multi-View Graph Clustering in complex networks. Multi-view graph clustering, an advanced form of graph clustering, leverages multiple perspectives or views of data to reveal hidden structures and communities within networks. This repository aims to be a comprehensive guide for researchers and practitioners exploring this fascinating area, covering a wide range of topics from foundational theories to state-of-the-art techniques.
Multi-view graph clustering is pivotal for understanding complex systems where entities interact in multiple contexts, such as:
- Social Networks: Analyzing diverse interactions like friendships, professional connections, and shared interests to better understand community structures.
- Biological Networks: Integrating various biological interactions, such as gene regulation, protein-protein interactions, and metabolic pathways, to uncover functional modules.
- Communication Networks: Studying different types of communications, such as email exchanges and social media interactions, to detect underlying group dynamics.
Whether you are a researcher looking to delve into the nuances of multi-view graph clustering, a practitioner seeking to apply these techniques to real-world problems, or a student eager to learn about this cutting-edge field, this repository is your go-to resource for everything multi-view graph clustering!
📚 Contents
Year | Title | CA | Institution | Venue | Paper | Code |
---|---|---|---|---|---|---|
2024 | Dual Information Enhanced Multi-view Attributed Graph Clustering | Chang-Dong Wang | Sun Yat-sen University | TNNLS | Link | Link |
2024 | Graph ClusteringContrastive Multiview Attribute Graph Clustering With Adaptive Encoders | Chang-Dong Wang | Sun Yat-sen University | TNNLS | Link | -- |
2024 | Multi-view attributed graph clustering based on graph diffusion convolution with adaptive fusion | Zhi hong Zhang | Zhengzhou University | ESWA | Link | -- |
2024 | Scalable and Structural Multi-view Graph Clustering with Adaptive Anchor Fusion | Xinwang Liu | National University of Defense Technology | TIP | Link | Link |
2024 | Multi-view fair-augmentation contrastive graph clustering with reliable pseudo-labels | Wei Xu | Renmin University | IS | Link | Link |
2023 | Sample-level Multi-view Graph Clustering | Shudong Huang | Sichuan University | CVPR | Link | Link |
2023 | Metric Multi-View Graph Clustering | Shudong Huang | Sichuan University | AAAI | Link | Link |
2023 | Clustering Information-guided Multi-view Contrastive Graph Clustering | Jinke Wang | Henan University | ICPADS | Link | Link |
2023 | Simultaneous linear multi-view attributed graph representation learning and clustering | Mohamed Nadif | Université Paris Cité | WSDM | Link | Link |
2023 | Deep multi-view graph clustering network with weighting mechanism and collaborative training | Fuyuan Cao | Shanxi University | ESWA | Link | -- |
2021 | Multi-view Contrastive Graph Clustering | Zhao Kang | University of Electronic Science and Technology of China | NIPS | Link | Link |
2021 | Graph Filter-based Multi-view Attributed Graph Clustering | Zhao Kang | University of Electronic Science and Technology of China | IJCAI | Link | Link |
2021 | Multi-view Attributed Graph Clustering | Zhao Kang | University of Electronic Science and Technology of China | TKDE | Link | Link |
2020 | Multi-View Attribute Graph Convolution Networks for Clustering | Quanxue Gao | Xidian University | IJCAI | Link | Link |
2019 | Contextual Correlation Preserving Multiview Featured Graph Clustering | Yang Liu | Hong Kong Baptist Universit | TCYB | Link | Link |
Links to publicly available multi-view graph datasets, including descriptions and recommended use cases.
Datasets | Views | Nodes | Edges | Attribute content | Features dimensions | Clusters | Link |
---|---|---|---|---|---|---|---|
ACM | 2 | 3025 | Co-Subject (29,281)|Co-Author (2,210,761) | Keywords of the paper | 1830 | 3 | Link |
DBLP | 3 | 4057 | Co-Author (11,113)|Co-Conference (5,000,495)|Co-Term (6,776,335) | Keywords of the author | 334 | 4 | Link |
IMDB | 2 | 4780 | Co-Actor (98,010)|Co-Director (21,018) | Keywords of the movie plot | 1232 | 3 | Link |
Cornell | 2 | 183 | 295 | The bag-of-words representation of the corresponding page | 1703 | 5 | Link Link |
Wisconsin | 2 | 251 | 499 | The bag-of-words representation of the corresponding page | 1703 | 5 | Link Link |
- A Comprehensive Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Happy researching! If you find this repository useful, feel free to star ⭐ it and contribute.