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 yueliu19990731@163.com. 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. ❤️
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.
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | A Comprehensive Survey on Community Detection with Deep Learning | TNNLS | Link | - |
| 2020 | A Comprehensive Survey on Graph Neural Networks | TNNLS | Link | - |
| 2020 | Deep Learning for Community Detection: Progress, Challenges and Opportunities | IJCAI | Link | - |
| 2018 | A survey of clustering with deep learning: From the perspective of network architecture | IEEE Access | Link | - |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering (FT-VGAE) | IJCAI | Link | Link |
| 2022 | Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering (R-GAE) | TKDE | Link | Link |
| 2022 | Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution (GEC-CSD) | NN | Link | Link |
| 2022 | Exploring temporal community structure via network embedding (VGRGMM) | TCYB | Link | - |
| 2022 | Cluster-Aware Heterogeneous Information Network Embedding (VaCA-HINE) | WSDM | Link | - |
| 2022 | Efficient Graph Convolution for Joint Node Representation Learning and Clustering (GCC) | WSDM | Link | Link |
| 2022 | ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations (scTAG) | AAAI | Link | Link |
| 2022 | Graph community infomax(GCI) | TKDD | Link | - |
| 2022 | Deep graph clustering with multi-level subspace fusion (DGCSF) | PR | Link | - |
| 2022 | Graph Clustering via Variational Graph Embedding (GC-VAE) | PR | Link | - |
| 2022 | Deep neighbor-aware embedding for node clustering in attributed graphs (DNENC) | PR | Link | - |
| 2022 | Collaborative Decision-Reinforced Self-Supervision for Attributed Graph Clustering (CDRS) | TNNLS | Link | Link |
| 2022 | Embedding Graph Auto-Encoder for Graph Clustering (EGAE) | TNNLS | Link | Link |
| 2021 | Self-Supervised Graph Convolutional Network for Multi-View Clustering (SGCMC) | TMM | Link | Link |
| 2021 | Adaptive Hypergraph Auto-Encoder for Relational Data Clustering (AHGAE) | TKDE | Link | - |
| 2021 | Deep Attention-guided Graph Clustering with Dual Self-supervision (DAGC) | arXiv | Link | Link |
| 2021 | Attention-driven Graph Clustering Network (AGCN) | ACM MM | Link | Link |
| 2021 | Deep Fusion Clustering Network (DFCN) | AAAI | Link | Link |
| 2020 | Graph Clustering with Graph Neural Networks (DMoN) | arXiv | Link | Link |
| 2020 | Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning (CGCN) | AAAI | Link | Link |
| 2020 | Deep multi-graph clustering via attentive cross-graph association (DMGC) | WSDM | Link | Link |
| 2020 | Going Deep: Graph Convolutional Ladder-Shape Networks (GCLN) | AAAI | Link | - |
| 2020 | Multi-view attribute graph convolution networks for clustering (MAGCN) | IJCAI | Link | Link |
| 2020 | One2Multi Graph Autoencoder for Multi-view Graph Clustering (O2MAC) | WWW | Link | Link |
| 2020 | Structural Deep Clustering Network (SDCN/SDCN_Q) | WWW | Link | Link |
| 2020 | Dirichlet Graph Variational Autoencoder (DGVAE) | NeurIPS | Link | Link |
| 2019 | RWR-GAE: Random Walk Regularization for Graph Auto Encoders (RWR-GAE) | arXiv | Link | Link |
| 2019 | Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning (GALA) | ICCV | Link | Link |
| 2019 | Attributed Graph Clustering: A Deep Attentional Embedding Approach (DAEGC) | IJCAI | Link | Link |
| 2019 | Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks (NetVAE) | IJCAI | Link | - |
| 2017 | MGAE: Marginalized Graph Autoencoder for Graph Clustering (MGAE) | CIKM | Link | Link |
| 2017 | Learning Community Embedding with Community Detection and Node Embedding on Graphs (ComE) | CIKM | Link | Link |
| 2016 | Deep Neural Networks for Learning Graph Representations (DNGR) | AAAI | Link | Link |
| 2015 | Heterogeneous Network Embedding via Deep Architectures (HNE) | SIGKDD | Link | - |
| 2014 | Learning Deep Representations for Graph Clustering (GraphEncoder) | AAAI | Link | Link |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2021 | Wasserstein Adversarially Regularized Graph Autoencoder | arXiv | Link | Link |
| 2020 | JANE: Jointly adversarial network embedding (JANE) | IJCAI | Link | - |
| 2019 | Adversarial Graph Embedding for Ensemble Clustering (AGAE) | IJCAI | Link | - |
| 2019 | CommunityGAN: Community Detection with Generative Adversarial Nets (CommunityGAN) | WWW | Link | Link |
| 2019 | ProGAN: Network embedding via proximity generative adversarial network (ProGAN) | SIGKDD | Link | - |
| 2019 | Learning Graph Embedding with Adversarial Training Methods (ARGA/ARVGA) | TCYB | Link | Link |
| 2018 | Adversarially Regularized Graph Autoencoder for Graph Embedding (ARGA/ARVGA) | IJCAI | Link | Link |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering (NCAGC) | arXiv | Link | Link |
| 2022 | Simple Contrastive Graph Clustering (SCGC) | arXiv | Link | - |
| 2022 | SCGC : Self-Supervised Contrastive Graph Clustering (SCGC) | arXiv | Link | Link |
| 2022 | Improved Dual Correlation Reduction Network (IDCRN) | arXiv | Link | - |
| 2022 | S3GC: Scalable Self-Supervised Graph Clustering (S3GC) | NeurIPS | Link | Link |
| 2022 | Self-consistent Contrastive Attributed Graph Clustering with Pseudo-label Prompt (SCAGC) | TMM | Link | Link |
| 2022 | CGC: Contrastive Graph Clustering for Community Detection and Tracking (CGC) | WWW | Link | - |
| 2022 | Towards Unsupervised Deep Graph Structure Learning (SUBLIME) | WWW | Link | Link |
| 2022 | Attributed Graph Clustering with Dual Redundancy Reduction (AGC-DRR) | IJCAI | Link | - |
| 2022 | Deep Graph Clustering via Dual Correlation Reduction (DCRN) | AAAI | Link | Link |
| 2022 | RepBin: Constraint-Based Graph Representation Learning for Metagenomic Binning (RepBin) | AAAI | Link | Link |
| 2022 | Augmentation-Free Self-Supervised Learning on Graphs (AFGRL) | AAAI | Link | Link |
| 2022 | SAIL: Self-Augmented Graph Contrastive Learning (SAIL) | AAAI | Link | - |
| 2021 | Graph Debiased Contrastive Learning with Joint Representation Clustering (GDCL) | IJCAI | Link | Link |
| 2021 | Multi-view Contrastive Graph Clustering (MCGC) | NeurIPS | Link | Link |
| 2021 | Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning (HeCo) | SIGKDD | Link | Link |
| 2020 | Adaptive Graph Encoder for Attributed Graph Embedding (AGE) | SIGKDD | Link | Link |
| 2020 | CommDGI: Community Detection Oriented Deep Graph Infomax (CommDGI) | CIKM | Link | Link |
| 2020 | Contrastive Multi-View Representation Learning on Graphs (MVGRL) | ICML | Link | Link |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | Twin Contrastive Learning for Online Clustering | IJCV | Link | Link |
| 2022 | Ada-nets: Face clustering via adaptive neighbor discovery in the structure space | ICLR | Link | Link |
| 2021 | Adaptive Graph Auto-Encoder for General Data Clustering | TPAMI | Link | Link |
| 2021 | Contrastive Clustering | AAAI | Link | Link |
| 2017 | Towards k-means-friendly spaces: Simultaneous deep learning and clustering | ICML | Link | Link |
| 2017 | Improved Deep Embedded Clustering with Local Structure Preservation | IJCAI | Link | Link |
| 2016 | Unsupervised Deep Embedding for Clustering Analysis | ICML | Link | Link |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | Deep linear graph attention model for attributed graph clustering | Knowl Based Syst | Link | - |
| 2022 | Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning | WWW | Link | - |
| 2022 | X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning (X-GOAL) | arXiv | Link | - |
| 2022 | Deep Graph Clustering with Multi-Level Subspace Fusion | PR | Link | - |
| 2022 | GRACE: A General Graph Convolution Framework for Attributed Graph Clustering | TKDD | Link | - |
| 2022 | Fine-grained Attributed Graph Clustering | SDM | Link | Link |
| 2022 | Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation | NN | Link | Link |
| 2022 | SAGES: Scalable Attributed Graph Embedding with Sampling for Unsupervised Learning | TKDE | Link | - |
| 2022 | Automated Self-Supervised Learning For Graphs | ICLR | Link | Link |
| 2022 | Stationary diffusion state neural estimation for multi-view clustering | AAAI | Link | Link |
| 2021 | Simple Spectral Graph Convolution | ICLR | Link | Link |
| 2021 | Spectral embedding network for attributed graph clustering (SENet) | NN | Link | - |
| 2021 | Smoothness Sensor: Adaptive Smoothness Transition Graph Convolutions for Attributed Graph Clustering | TCYB | Link | Link |
| 2021 | Multi-view Attributed Graph Clustering | TKDE | Link | Link |
| 2021 | High-order Deep Multiplex Infomax | WWW | Link | Link |
| 2021 | Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs | PAKDD | Link | Link |
| 2021 | Graph Filter-based Multi-view Attributed Graph Clustering | IJCAI | Link | Link |
| 2021 | Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs | arXiv | Link | Link |
| 2021 | Contrastive Laplacian Eigenmaps | NeurIPS | Link | Link |
| 2020 | Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning | arXiv | Link | - |
| 2020 | Distribution-induced Bidirectional GAN for Graph Representation Learning | CVPR | Link | Link |
| 2020 | Adaptive Graph Converlutional Network with Attention Graph Clustering for Co saliency Detection | CVPR | Link | Link |
| 2020 | Spectral Clustering with Graph Neural Networks for Graph Pooling (MinCutPool) | ICML | Link | Link |
| 2020 | MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding | WWW | Link | Link |
| 2020 | Unsupervised Attributed Multiplex Network Embedding | AAAI | Link | Link |
| 2020 | Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure | ICDM | Link | Link |
| 2020 | Multi-class imbalanced graph convolutional network learning | IJCAI | Link | - |
| 2020 | CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning | arXiv | Link | - |
| 2020 | Attributed Graph Clustering via Deep Adaptive Graph Maximization | ICCKE | Link | - |
| 2019 | Heterogeneous Graph Attention Network (HAN) | WWW | Link | Link |
| 2019 | Multi-view Consensus Graph Clustering | TIP | Link | Link |
| 2019 | Attributed Graph Clustering via Adaptive Graph Convolution (AGC) | IJCAI | Link | Link |
| 2016 | Variational Graph Auto-Encoders (GAE) | NeurIPS Workshop | Link | 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.
- 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
- utils.py
- load_graph_data: load graph datasets
- load_data: load non-graph datasets
- normalize_adj: normalize the adjacency matrix
- diffusion_adj: calculate the graph diffusion
- construct_graph: construct the knn graph for non-graph datasets
- numpy_to_torch: convert numpy to torch
- torch_to_numpy: convert torch to numpy
- clustering.py
- setup_seed: fix the random seed
- evaluation: evaluate the performance of clustering
- k_means: K-means algorithm
- visualization.py
- t_sne: t-SNE algorithm
- similarity_plot: visualize cosine similarity matrix of the embedding or feature
About the introduction of each dataset, please check here
-
Graph Datasets
Dataset # Samples # Dimension # Edges # Classes URL CORA 2708 1433 5278 7 cora.zip CITESEER 3327 3703 4552 6 citeseer.zip CITE 3327 3703 4552 6 cite.zip PUBMED 19717 500 44324 3 pubmed.zip DBLP 4057 334 3528 4 dblp.zip ACM 3025 1870 13128 3 acm.zip AMAP 7650 745 119081 8 amap.zip AMAC 13752 767 245861 10 amac.zip CORAFULL 19793 8710 63421 70 corafull.zip WIKI 2405 4973 8261 17 wiki.zip COCS 18333 6805 81894 15 cocs.zip CORNELL 183 1703 149 5 cornell.zip TEXAS 183 1703 162 5 texas.zip WISC 251 1703 257 5 wisc.zip FILM 7600 932 15009 5 film.zip BAT 131 81 1038 4 bat.zip EAT 399 203 5994 4 eat.zip UAT 1190 239 13599 4 uat.zip
Edges: Here, we just count the number of undirected edges.
-
Non-graph Datasets
Dataset Samples Dimension Type Classes URL USPS 9298 256 Image 10 usps.zip HHAR 10299 561 Record 6 hhar.zip REUT 10000 2000 Text 4 reut.zip
@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}
}
@article{mrabah2021rethinking,
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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