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].

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

Papers

1. K-Means: "Algorithm AS 136: A k-means clustering algorithm" [pdf|code]
2. DCN (ICML17): "Towards k-means-friendly spaces: Simultaneous deep learning and clustering" [pdf|code]
3. DEC (ICML16): "Unsupervised Deep Embedding for Clustering Analysis" [pdf|code]
4. IDEC (IJCAI17): "Improved Deep Embedded Clustering with Local Structure Preservation" [pdf|code]
5. GAE/VGAE : "Variational Graph Auto-Encoders" [pdf|code]
6. DAEGC (IJCAI19): "Attributed Graph Clustering: A Deep Attentional Embedding Approach" [pdf|code]
7. ARGA/ARVGA (TCYB19): "Learning Graph Embedding with Adversarial Training Methods" [pdf|code]
8. SDCN/SDCN_Q (WWW20): "Structural Deep Clustering Network" [pdf|code]
9. DFCN (AAAI21): "Deep Fusion Clustering Network" [pdf|code]
10. MVGRL (ICML20): "Contrastive Multi-View Representation Learning on Graphs" [pdf|code]

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|Baidu Netdisk]. Optionally, download some of them from URLs in the tables (Google Drive)

• Step2: Unzip them to ./dataset/

• Step3: Run the ./dataset/utils.py

  Two functions load_graph_data and load_data are provided in ./dataset/utils.py to load graph datasets and non-graph datasets, respectively.

Datasets Details

1. Graph Datasets

   Dataset	Samples	Dimension	Edges	Classes	URL
   DBLP	4057	334	3528	4	dblp.zip
   CITE	3327	3703	4552	6	cite.zip
   ACM	3025	1870	13128	3	acm.zip
   AMAP	7650	745	119081	8	amap.zip
   AMAC	13752	767	245861	10	amac.zip
   PUBMED	19717	500	44325	3	pubmed.zip
   CORAFULL	19793	8710	63421	70	corafull.zip
   CORA	2708	1433	6632	7	cora.zip
   CITESEER	3327	3703	6215	6	citeseer.zip

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

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