ADGC: Awesome Deep Graph Clustering

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. :sparkles: If you use our code or the processed datasets in this repository for your research, please cite 2-3 papers in the citation part here. :heart:

What's Deep Graph Clustering?

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

Important Survey Papers

Papers

Temporal Deep Graph Clustering

Reconstructive Deep Graph Clustering

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Adversarial Deep Graph Clustering

Contrastive Deep Graph Clustering

Application

Others

Other Related Papers

Deep Clustering

Deep Hierarchical Clustering

Other Related Methods

Benchmark Datasets

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.

Quick Start

Step1: Download all datasets from [Google Drive | Nutstore]. Optionally, download some of them from URLs in the tables (Google Drive)

Step2: Unzip them to ./dataset/

Step3: Change the type and the name of the dataset in main.py

Step4: Run the main.py

Code

utils.py

1. load_graph_data: load graph datasets
2. load_data: load non-graph datasets
3. normalize_adj: normalize the adjacency matrix
4. diffusion_adj: calculate the graph diffusion
5. construct_graph: construct the knn graph for non-graph datasets
6. numpy_to_torch: convert numpy to torch
7. torch_to_numpy: convert torch to numpy

clustering.py

1. setup_seed: fix the random seed
2. evaluation: evaluate the performance of clustering
3. k_means: K-means algorithm

visualization.py

1. t_sne: t-SNE algorithm
2. similarity_plot: visualize cosine similarity matrix of the embedding or feature

Datasets Details

About the introduction of each dataset, please check here

1. Graph Datasets

Edges: Here, we just count the number of undirected edges.

2. Non-graph Datasets

Citation

@article{deep_graph_clustering_survey,
title={A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application},
author={Liu, Yue and Xia, Jun and Zhou, Sihang and Wang, Siwei and Guo, Xifeng and Yang, Xihong and Liang, Ke and Tu, Wenxuan and Li, Z. Stan and Liu, Xinwang},
journal={arXiv preprint arXiv:2211.12875},
year={2022}
}

@article{SCGC,
title={Simple contrastive graph clustering},
author={Liu, Yue and Yang, Xihong and Zhou, Sihang and Liu, Xinwang and Wang, Siwei and Liang, Ke and Tu, Wenxuan and Li, Liang},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2023},
publisher={IEEE}
}

@inproceedings{Dink_Net,
title={Dink-net: Neural clustering on large graphs},
author={Liu, Yue and Liang, Ke and Xia, Jun and Zhou, Sihang and Yang, Xihong and Liu, Xinwang and Li, Stan Z},
booktitle={Proceedings of International Conference on Machine Learning},
year={2023}
}

@inproceedings{TGC_ML_ICLR,
title={Deep Temporal Graph Clustering},
author={Liu, Meng and Liu, Yue and Liang, Ke and Tu, Wenxuan and Wang, Siwei and Zhou, Sihang and Liu, Xinwang},
booktitle={The 12th International Conference on Learning Representations},
year={2024}
}

@inproceedings{HSAN,
title={Hard sample aware network for contrastive deep graph clustering},
author={Liu, Yue and Yang, Xihong and Zhou, Sihang and Liu, Xinwang and Wang, Zhen and Liang, Ke and Tu, Wenxuan and Li, Liang and Duan, Jingcan and Chen, Cancan},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={37},
number={7},
pages={8914-8922},
year={2023}
}

@inproceedings{DCRN,
title={Deep Graph Clustering via Dual Correlation Reduction},
author={Liu, Yue and Tu, Wenxuan and Zhou, Sihang and Liu, Xinwang and Song, Linxuan and Yang, Xihong and Zhu, En},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={36},
number={7},
pages={7603-7611},
year={2022}
}


@inproceedings{liuyue_RGC,
title={Reinforcement Graph Clustering with Unknown Cluster Number},
author={Liu, Yue and Liang, Ke and Xia, Jun and Yang, Xihong and Zhou, Sihang and Liu, Meng and Liu, Xinwang and Li, Stan Z},
booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
pages={3528--3537},
year={2023}
}




@article{RGAE,
title={Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering},
author={Mrabah, Nairouz and Bouguessa, Mohamed and Touati, Mohamed Fawzi and Ksantini, Riadh},
journal={IEEE Transactions on Knowledge and Data Engineering},
year={2022}
}
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