Reinforcement Graph Clustering with Unknown Cluster Number
To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Rein...
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GitHub Link
The GitHub link is https://github.com/yueliu1999/awesome-deep-graph-clustering
Introduce
The GitHub repository “Awesome Deep Graph Clustering” (ADGC) contains a comprehensive collection of state-of-the-art deep graph clustering methods, including papers, codes, and datasets. The repository covers various aspects of deep graph clustering, aiming to reveal underlying graph structures and group nodes into different clusters. It offers survey papers, taxonomy, challenge explanations, and applications. The methods span across different categories like Reconstructive, Adversarial, and Contrastive deep graph clustering, each having numerous papers, codes, and papers. Various benchmark datasets, both graph and non-graph types, are also included. The repository is curated by yueliu1999 and provides a valuable resource for researchers and practitioners interested in deep graph clustering techniques.
To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
Content
ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets). Any other interesting papers and codes are welcome. Any problems, please contact [email protected]. If you find this repository useful to your research or work, it is really appreciated to star this repository. _ If you use our code or the processed datasets in this repository for your research, please cite 1-2 papers in the citation part here. __ Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. More details can be found in the survey paper. Link We divide the datasets into two categories, i.e. graph datasets and non-graph datasets. Graph datasets are some graphs in real-world, such as citation networks, social networks and so on. Non-graph datasets are NOT graph type. However, if necessary, we could construct “adjacency matrices” by K-Nearest Neighbors (KNN) algorithm. About the introduction of each dataset, please check here Edges: Here, we just count the number of undirected edges. A Unified Framework for Deep Attribute Graph Clustering
Related
To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy.
