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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. :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:

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What is 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

<div align="center"> <img src="./assets/logo_new.png" width=90% /> </div>

Important Survey Papers

YearTitleVenuePaperCode
2023An Overview of Advanced Deep Graph Node ClusteringTCSSLink-
2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, and ApplicationarXivLinkLink
2022A Comprehensive Survey on Community Detection with Deep LearningTNNLSLink-
2020A Comprehensive Survey on Graph Neural NetworksTNNLSLink-
2020Deep Learning for Community Detection: Progress, Challenges and OpportunitiesIJCAILink-
2018A survey of clustering with deep learning: From the perspective of network architectureIEEE AccessLink-

Papers

New-architecture Deep Graph Clustering

YearTitleVenuePaperCode
2024Kolmogorov-Arnold Network (KAN) for Graphs--link

Temporal Deep Graph Clustering

YearTitleVenuePaperCode
2024Deep Temporal Graph Clustering (TGC)ICLRLinklink

Deep Graph Clustering with Unknown Cluster Number

YearTitleVenuePaperCode
2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering (LSEnet)ICMLLinkLink
2024Masked AutoEncoder for Graph Clustering without Pre-defined Cluster Number k (GCMA)arXivLink-
2023Reinforcement Graph Clustering with Unknown Cluster Number (RGC)ACM MMLinkLink

Reconstructive Deep Graph Clustering

YearTitleVenuePaperCode
2024Synergistic Deep Graph Clustering Network (SynC)Arxivlinklink
2024Deep Masked Graph Node Clustering （DMGC）TCSSlink-
2024Multi-scale graph clustering network (MGCN)ISlinklink
2024An End-to-End Deep Graph Clustering via Online Mutual LearningTNNLSlink-
2024Contrastive Deep Nonnegative Matrix Factorization for Community Detection (CDNMF)ICASSPlinklink
2023EGRC-Net: Embedding-Induced Graph Refinement Clustering Network (EGRC-Net)TIPLinkLink
2023Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node Clustering (BELBO-VGAE)SDMLinkLink
2023Graph Clustering with Graph Neural Networks (DMoN)JMLRLinkLink
2023Graph Clustering Network with Structure Embedding Enhanced (GC-SEE)PRlinklink
2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering (DGCN)ICMLLinkLink
2023Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data (scTCM)IJCAILinkLink
2023Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-cell RNA-seq: A Unified Perspective (scTPF)AAAILinkLink
2022Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering (FT-VGAE)IJCAILinkLink
2022Deep Attention-guided Graph Clustering with Dual Self-supervision (DAGC)TCSVTLinkLink
2022Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering (R-GAE)TKDELinkLink
2022Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution (GEC-CSD)NNLink-
2022Exploring temporal community structure via network embedding (VGRGMM)TCYBLink-
2022Cluster-Aware Heterogeneous Information Network Embedding (VaCA-HINE)WSDMLink-
2022Efficient Graph Convolution for Joint Node Representation Learning and Clustering (GCC)WSDMLinkLink
2022ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations (scTAG)AAAILinkLink
2022Graph community infomax(GCI)TKDDLink-
2022Deep graph clustering with multi-level subspace fusion (DGCSF)PRLink-
2022Graph Clustering via Variational Graph Embedding (GC-VAE)PRLink-
2022Deep neighbor-aware embedding for node clustering in attributed graphs (DNENC)PRLink-
2022Collaborative Decision-Reinforced Self-Supervision for Attributed Graph Clustering (CDRS)TNNLSLinkLink
2022Embedding Graph Auto-Encoder for Graph Clustering (EGAE)TNNLSLinkLink
2021Self-Supervised Graph Convolutional Network for Multi-View Clustering (SGCMC)TMMLinkLink
2021Adaptive Hypergraph Auto-Encoder for Relational Data Clustering (AHGAE)TKDELink-
2021Attention-driven Graph Clustering Network (AGCN)ACM MMLinkLink
2021Deep Fusion Clustering Network (DFCN)AAAILinkLink
2020Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning (CGCN)AAAILinkLink
2020Deep multi-graph clustering via attentive cross-graph association (DMGC)WSDMLinkLink
2020Going Deep: Graph Convolutional Ladder-Shape Networks (GCLN)AAAILink-
2020Multi-view attribute graph convolution networks for clustering (MAGCN)IJCAILinkLink
2020One2Multi Graph Autoencoder for Multi-view Graph Clustering (O2MAC)WWWLinkLink
2020Structural Deep Clustering Network (SDCN/SDCN_Q)WWWLinkLink
2020Dirichlet Graph Variational Autoencoder (DGVAE)NeurIPSLinkLink
2019RWR-GAE: Random Walk Regularization for Graph Auto Encoders (RWR-GAE)arXivLinkLink
2019Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning (GALA)ICCVLinkLink
2019Attributed Graph Clustering: A Deep Attentional Embedding Approach (DAEGC)IJCAILinkLink
2019Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks (NetVAE)IJCAILink-
2017Graph Clustering with Dynamic Embedding (GRACE)arXivLinkLink
2017MGAE: Marginalized Graph Autoencoder for Graph Clustering (MGAE)CIKMLinkLink
2017Learning Community Embedding with Community Detection and Node Embedding on Graphs (ComE)CIKMLinkLink
2016Deep Neural Networks for Learning Graph Representations (DNGR)AAAILinkLink
2015Heterogeneous Network Embedding via Deep Architectures (HNE)SIGKDDLink-
2014Learning Deep Representations for Graph Clustering (GraphEncoder)AAAILinkLink

Adversarial Deep Graph Clustering

YearTitleVenuePaperCode
2023Wasserstein Adversarially Regularized Graph Autoencoder (WARGA)NeurocomputingLinkLink
2022Unsupervised network embedding beyond homophily (SELENE)TMLRLinkLink
2020JANE: Jointly adversarial network embedding (JANE)IJCAILink-
2019Adversarial Graph Embedding for Ensemble Clustering (AGAE)IJCAILink-
2019CommunityGAN: Community Detection with Generative Adversarial Nets (CommunityGAN)WWWLinkLink
2019ProGAN: Network embedding via proximity generative adversarial network (ProGAN)SIGKDDLink-
2019Learning Graph Embedding with Adversarial Training Methods (ARGA/ARVGA)TCYBLinkLink
2019Adversarially Regularized Graph Autoencoder for Graph Embedding (ARGA/ARVGA)IJCAILinkLink

Contrastive Deep Graph Clustering

YearTitleVenuePaperCode
2024Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning PerspectiveSIGKDDlinklink
2024GLAC-GCN: Global and Local Topology-Aware Contrastive Graph Clustering Network (GLAC-GCN)TAIlinklink
2024Contrastive Multiview Attribute Graph Clustering With Adaptive EncodersTNNLSlink-
2024Contrastive Deep Nonnegative Matrix Factorization for Community Detection (CDNMF)ICASSPlinklink
2023A Contrastive Variational Graph Auto-Encoder for Node Clustering (CVGAE)PRLinkLink
2023Dual Contrastive Learning Network for Graph ClusteringTNNLSLinkLink
2023Contrastive Learning with Cluster-Preserving Augmentation for Attributed Graph ClusteringECML-PKDDLink-
2023Graph Contrastive Representation Learning with Input-Aware and Cluster-Aware RegularizationECML-PKDDLink-
2023Reinforcement Graph Clustering with Unknown Cluster Number (RGC)ACM MMLinkLink
2023Self-Contrastive Graph Diffusion NetworkACM MMLinkLink
2023CONVERT: Contrastive Graph Clustering with Reliable Augmentation (CONVERT)ACM MMLinkLink
2023Attribute Graph Clustering via Learnable Augmentation (AGCLA)arXivLink-
2023CARL-G: Clustering-Accelerated Representation Learning on Graphs (CARL-G)SIGKDDLink-
2023Dink-Net: Neural Clustering on Large Graphs (Dink-Net)ICMLLinkLink
2023CONGREGATE: Contrastive Graph Clustering in Curvature Spaces (CONGREGATE)IJCAILinkLink
2023Multi-level Graph Contrastive Prototypical ClusteringIJCAILink-
2023Simple Contrastive Graph Clustering (SCGC)TNNLSLinkLink
2023Hard Sample Aware Network for Contrastive Deep Graph Clustering (HSAN)AAAILinkLink
2023Cluster-guided Contrastive Graph Clustering Network (CCGC)AAAILinkLink
2022NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering (NCAGC)arXivLinkLink
2022SCGC : Self-Supervised Contrastive Graph Clustering (SCGC)arXivLinkLink
2022Improved Dual Correlation Reduction Network (IDCRN)arXivLink-
2022Towards Self-supervised Learning on Graphs with Heterophily (HGRL)CIKMLinkLink
2022S3GC: Scalable Self-Supervised Graph Clustering (S3GC)NeurIPSLinkLink
2022Self-consistent Contrastive Attributed Graph Clustering with Pseudo-label Prompt (SCAGC)TMMLinkLink
2022CGC: Contrastive Graph Clustering for Community Detection and Tracking (CGC)WWWLink-
2022Towards Unsupervised Deep Graph Structure Learning (SUBLIME)WWWLinkLink
2022Attributed Graph Clustering with Dual Redundancy Reduction (AGC-DRR)IJCAILinkLink
2022Deep Graph Clustering via Dual Correlation Reduction (DCRN)AAAILinkLink
2022RepBin: Constraint-Based Graph Representation Learning for Metagenomic Binning (RepBin)AAAILinkLink
2022Augmentation-Free Self-Supervised Learning on Graphs (AFGRL)AAAILinkLink
2022SAIL: Self-Augmented Graph Contrastive Learning (SAIL)AAAILink-
2021Graph Debiased Contrastive Learning with Joint Representation Clustering (GDCL)IJCAILinkLink
2021Multi-view Contrastive Graph Clustering (MCGC)NeurIPSLinkLink
2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning (HeCo)SIGKDDLinkLink
2020Adaptive Graph Encoder for Attributed Graph Embedding (AGE)SIGKDDLinkLink
2020CommDGI: Community Detection Oriented Deep Graph Infomax (CommDGI)CIKMLinkLink
2020Contrastive Multi-View Representation Learning on Graphs (MVGRL)ICMLLinkLink

Application

YearTitleVenuePaperCode
2024End-to-end Learnable Clustering for Intent Learning in Recommendation (ELCRec)NeurIPSLinkLink
2023GuardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph ClusteringarXivLink-

Others

YearTitleVenuePaperCode
2023Robust Graph Clustering via Meta Learning for Noisy Graphs (MetaGC)CIKMLinkLink

Other Related Papers

Deep Clustering

YearTitleVenuePaperCode
2024ProCom: A Few-shot Targeted Community Detection AlgorithmAAAILinkLink
2024Deep graph clustering by integrating community structure with neighborhood information (DIGC)ISLink-
2024Information-enhanced deep graph clustering network (IEDGCN)NeurocomputingLink-
2024Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph ClusteringAAAILinkLink
2024DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization (DGCluster)AAAILink-
2023Mutual Boost Network for attributed graph clustering (MBN)KBSLink-
2023Redundancy-Free Self-Supervised Relational Learning for Graph ClusteringTNNLSLinkLink
2023Spectral Clustering of Attributed Multi-relational GraphsSIGKDDLink-
2023Local Graph Clustering with Noisy LabelsArxivLink-
2023A Re-evaluation of Deep Learning Methods for Attributed Graph ClusteringCIKMLinkLink
2023Robust Graph Clustering via Meta Weighting for Noisy GraphsCIKMLinkLink
2023Homophily-enhanced Structure Learning for Graph ClusteringCIKMLinkLink
2023A Re-evaluation of Deep Learning Methodsfor Attributed Graph ClusteringCIKMLinkLink
2023Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node ClusteringSDMLinkLink
2023GC-Flow: A Graph-Based Flow Network for Effective ClusteringICLMLinkLink
2023Scalable Attributed-Graph Subspace Clustering (SAGSC)AAAILinkLink
2022Adaptive Attribute and Structure Subspace Clustering Network (AASSC-Net)TIPLinkLink
2022Twin Contrastive Learning for Online ClusteringIJCVLinkLink
2022Non-Graph Data Clustering via O(n) Bipartite Graph ConvolutionTPAMILinkLink
2022Ada-nets: Face clustering via adaptive neighbor discovery in the structure spaceICLRLinkLink
2021Adaptive Graph Auto-Encoder for General Data ClusteringTPAMILinkLink
2021Contrastive ClusteringAAAILinkLink
2017Towards k-means-friendly spaces: Simultaneous deep learning and clustering (DCN)ICMLLinkLink
2017Improved Deep Embedded Clustering with Local Structure Preservation (IDEC)IJCAILinkLink
2016Unsupervised Deep Embedding for Clustering Analysis (DEC)ICMLLinkLink

Deep Hierarchical Clustering

YearTitleVenuePaperCode
2023Contrastive Hierarchical Clustering (CHC)ECML PKDDLinkLink

Other Related Methods

YearTitleVenuePaperCode
2024Effective Clustering on Large Attributed Bipartite Graphs (TPO)arXivLink-
2023GPUSCAN++: Efficient Structural Graph Clustering on GPUsarXivLink-
2022Deep linear graph attention model for attributed graph clusteringKnowl Based SystLink-
2022Scalable Deep Graph Clustering with Random-walk based Self-supervised LearningWWWLink-
2022X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning (X-GOAL)arXivLink-
2022Deep Graph Clustering with Multi-Level Subspace FusionPRLink-
2022GRACE: A General Graph Convolution Framework for Attributed Graph ClusteringTKDDLinkLink
2022Fine-grained Attributed Graph ClusteringSDMLinkLink
2022Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representationNNLinkLink
2022SAGES: Scalable Attributed Graph Embedding with Sampling for Unsupervised LearningTKDELink-
2022Automated Self-Supervised Learning For GraphsICLRLinkLink
2022Stationary diffusion state neural estimation for multi-view clusteringAAAILinkLink
2021Simple Spectral Graph ConvolutionICLRLinkLink
2021Spectral embedding network for attributed graph clustering (SENet)NNLink-
2021Smoothness Sensor: Adaptive Smoothness Transition Graph Convolutions for Attributed Graph ClusteringTCYBLinkLink
2021Multi-view Attributed Graph ClusteringTKDELinkLink
2021High-order Deep Multiplex InfomaxWWWLinkLink
2021Graph InfoClust: Maximizing Coarse-Grain Mutual Information in GraphsPAKDDLinkLink
2021Graph Filter-based Multi-view Attributed Graph ClusteringIJCAILinkLink
2021Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex GraphsarXivLinkLink
2021Contrastive Laplacian EigenmapsNeurIPSLinkLink
2020Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation LearningarXivLink-
2020Distribution-induced Bidirectional GAN for Graph Representation LearningCVPRLinkLink
2020Adaptive Graph Converlutional Network with Attention Graph Clustering for Co saliency DetectionCVPRLinkLink
2020Spectral Clustering with Graph Neural Networks for Graph Pooling (MinCutPool)ICMLLinkLink
2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph EmbeddingWWWLinkLink
2020Unsupervised Attributed Multiplex Network EmbeddingAAAILinkLink
2020Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted StructureICDMLinkLink
2020Multi-class imbalanced graph convolutional network learningIJCAILink-
2020CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation LearningarXivLink-
2020Attributed Graph Clustering via Deep Adaptive Graph MaximizationICCKELink-
2019Heterogeneous Graph Attention Network (HAN)WWWLinkLink
2019Multi-view Consensus Graph ClusteringTIPLinkLink
2019Attributed Graph Clustering via Adaptive Graph Convolution (AGC)IJCAILinkLink
2016node2vec: Scalable Feature Learning for Networks (node2vec)SIGKDDLinkLink
2016Variational Graph Auto-Encoders (GAE)NeurIPS WorkshopLinkLink
2015LINE: Large-scale Information Network Embedding (LINE)WWWLinkLink
2014DeepWalk: Online Learning of Social Representations (DeepWalk)SIGKDDLinkLink

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

Code

Datasets Details

About the introduction of each dataset, please check here

  1. Graph Datasets

    Dataset# Samples# Dimension# Edges# ClassesURL
    CORA2708143352787cora.zip
    CITESEER3327370345526citeseer.zip
    CITE3327370345526cite.zip
    PUBMED19717500443243pubmed.zip
    DBLP405733435284dblp.zip
    ACM30251870131283acm.zip
    AMAP76507451190818amap.zip
    AMAC1375276724586110amac.zip
    CORAFULL1979387106342170corafull.zip
    WIKI24054973826117wiki.zip
    COCS1833368058189415cocs.zip
    CORNELL18317031495cornell.zip
    TEXAS18317031625texas.zip
    WISC25117032575wisc.zip
    FILM7600932150095film.zip
    BAT1318110384bat.zip
    EAT39920359944eat.zip
    UAT1190239135994uat.zip

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

  1. Non-graph Datasets

    DatasetSamplesDimensionTypeClassesURL
    USPS9298256Image10usps.zip
    HHAR10299561Record6hhar.zip
    REUT100002000Text4reut.zip

Citation


@inproceedings{ITR,
  title={Identify Then Recommend: Towards Unsupervised Group Recommendation},
  author={Liu, Yue and Zhu, Shihao and Yang, Tianyuan and Ma, Jian and Zhong, Wenliang},
  booktitle={Proc. of NeurIPS},
  year={2024}
}

@article{ELCRec,
  title={End-to-end Learnable Clustering for Intent Learning in Recommendation},
  author={Liu, Yue and Zhu, Shihao and Xia, Jun and Ma, Yingwei and Ma, Jian and Zhong, Wenliang and Zhang, Guannan and Zhang, Kejun and Liu, Xinwang},
  booktitle={Proceedings of International Conference on Neural Information Processing Systems},
  year={2024}
}

@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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