farmingtong/awesome-deep-graph-clustering

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

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. ✨ If you use our code or the processed datasets in this repository for your research, please cite 1-2 papers in the citation part. ❤️

---

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.

Important Survey Papers

Year	Title	Venue	Paper	Code
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

Generative Deep Graph Clustering

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

Adversarial Deep Graph Clustering

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

Contrastive Deep Graph Clustering

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

Other Related Papers

Deep Clustering

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

Other Related Methods

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

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.

2. 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}
}
@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}
}

Other Related Awesome Repository

awesome-awesome-machine-learning

Awesome Incomplete Graph Representation Learning

awesome-self-supervised-gnn