yueliu1999 / Awesome-Deep-Graph-Clustering

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

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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 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 2-3 papers in the citation part here. ❤️

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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

Year Title Venue Paper Code
2023 An Overview of Advanced Deep Graph Node Clustering TCSS Link -
2022 A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application arXiv Link Link
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 -

Papers

Temporal Deep Graph Clustering

Year Title Venue Paper Code
2024 Deep Temporal Graph Clustering (TGC) ICLR Link link
2023 arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering (arXiv4TGC) arXiv Link Link

Reconstructive Deep Graph Clustering

Year Title Venue Paper Code
2024 Contrastive Deep Nonnegative Matrix Factorization for Community Detection (CDNMF) ICASSP link link
2023 EGRC-Net: Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) TIP Link Link
2023 Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node Clustering (BELBO-VGAE) SDM Link Link
2023 Graph Clustering with Graph Neural Networks (DMoN) JMLR Link Link
2023 Graph Clustering Network with Structure Embedding Enhanced (GC-SEE) PR link link
2023 Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering (DGCN) ICML Link Link
2023 Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data (scTCM) IJCAI Link Link
2023 Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-cell RNA-seq: A Unified Perspective (scTPF) AAAI Link Link
2022 Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering (FT-VGAE) IJCAI Link Link
2022 Deep Attention-guided Graph Clustering with Dual Self-supervision (DAGC) TCSVT 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 -
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 Attention-driven Graph Clustering Network (AGCN) ACM MM Link Link
2021 Deep Fusion Clustering Network (DFCN) AAAI 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 Graph Clustering with Dynamic Embedding (GRACE) arXiv Link 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

Adversarial Deep Graph Clustering

Year Title Venue Paper Code
2023 Wasserstein Adversarially Regularized Graph Autoencoder (WARGA) Neurocomputing Link Link
2022 Unsupervised network embedding beyond homophily (SELENE) TMLR 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
2019 Adversarially Regularized Graph Autoencoder for Graph Embedding (ARGA/ARVGA) IJCAI Link Link

Contrastive Deep Graph Clustering

Year Title Venue Paper Code
2024 Contrastive Deep Nonnegative Matrix Factorization for Community Detection (CDNMF) ICASSP link link
2023 A Contrastive Variational Graph Auto-Encoder for Node Clustering (CVGAE) PR Link Link
2023 Dual Contrastive Learning Network for Graph Clustering TNNLS Link Link
2023 Contrastive Learning with Cluster-Preserving Augmentation for Attributed Graph Clustering ECML-PKDD Link -
2023 Graph Contrastive Representation Learning with Input-Aware and Cluster-Aware Regularization ECML-PKDD Link -
2023 Reinforcement Graph Clustering with Unknown Cluster Number (RGC) ACM MM Link Link
2023 Self-Contrastive Graph Diffusion Network ACM MM Link Link
2023 CONVERT: Contrastive Graph Clustering with Reliable Augmentation (CONVERT) ACM MM Link Link
2023 Attribute Graph Clustering via Learnable Augmentation (AGCLA) arXiv Link -
2023 CARL-G: Clustering-Accelerated Representation Learning on Graphs (CARL-G) SIGKDD Link -
2023 Dink-Net: Neural Clustering on Large Graphs (Dink-Net) ICML Link Link
2023 CONGREGATE: Contrastive Graph Clustering in Curvature Spaces (CONGREGATE) IJCAI Link Link
2023 Multi-level Graph Contrastive Prototypical Clustering IJCAI Link -
2023 Simple Contrastive Graph Clustering (SCGC) TNNLS Link Link
2023 Hard Sample Aware Network for Contrastive Deep Graph Clustering (HSAN) AAAI Link Link
2023 Cluster-guided Contrastive Graph Clustering Network (CCGC) AAAI Link Link
2022 NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering (NCAGC) arXiv Link Link
2022 SCGC : Self-Supervised Contrastive Graph Clustering (SCGC) arXiv Link Link
2022 Improved Dual Correlation Reduction Network (IDCRN) arXiv Link -
2022 Towards Self-supervised Learning on Graphs with Heterophily (HGRL) CIKM Link 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 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

Application

Year Title Venue Paper Code
2024 End-to-end Learnable Clustering for Intent Learning in Recommendation arXiv Link Link
2023 GuardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering arXiv Link -

Others

Year Title Venue Paper Code
2023 Robust Graph Clustering via Meta Learning for Noisy Graphs (MetaGC) CIKM Link Link

Other Related Papers

Deep Clustering

Year Title Venue Paper Code
2023 Mutual Boost Network for attributed graph clustering (MBN) KBS Link -
2023 Redundancy-Free Self-Supervised Relational Learning for Graph Clustering TNNLS Link Link
2023 Spectral Clustering of Attributed Multi-relational Graphs SIGKDD Link -
2023 Local Graph Clustering with Noisy Labels Arxiv Link -
2023 A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering CIKM Link Link
2023 Robust Graph Clustering via Meta Weighting for Noisy Graphs CIKM Link Link
2023 Homophily-enhanced Structure Learning for Graph Clustering CIKM Link Link
2023 A Re-evaluation of Deep Learning Methodsfor Attributed Graph Clustering CIKM Link Link
2023 Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node Clustering SDM Link Link
2023 GC-Flow: A Graph-Based Flow Network for Effective Clustering ICLM Link Link
2023 Scalable Attributed-Graph Subspace Clustering (SAGSC) AAAI Link Link
2022 Adaptive Attribute and Structure Subspace Clustering Network (AASSC-Net) TIP Link Link
2022 Twin Contrastive Learning for Online Clustering IJCV Link Link
2022 Non-Graph Data Clustering via O(n) Bipartite Graph Convolution TPAMI 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 (DCN) ICML Link Link
2017 Improved Deep Embedded Clustering with Local Structure Preservation (IDEC) IJCAI Link Link
2016 Unsupervised Deep Embedding for Clustering Analysis (DEC) ICML Link Link

Deep Hierarchical Clustering

Year Title Venue Paper Code
2023 Contrastive Hierarchical Clustering (CHC) ECML PKDD Link Link

Other Related Methods

Year Title Venue Paper Code
2023 GPUSCAN++: Efficient Structural Graph Clustering on GPUs arXiv Link -
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 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 node2vec: Scalable Feature Learning for Networks (node2vec) SIGKDD Link Link
2016 Variational Graph Auto-Encoders (GAE) NeurIPS Workshop Link Link
2015 LINE: Large-scale Information Network Embedding (LINE) WWW Link Link
2014 DeepWalk: Online Learning of Social Representations (DeepWalk) SIGKDD Link Link

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

    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.

  1. 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

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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Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

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