Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

A curated list of papers on graph pooling (More than 150 papers reviewed).

---

We provide a taxonomy of existing papers as shown in the above figure.

Papers in each category are sorted by their uploaded dates in descending order.

If you find this repository useful for your research, please consider citing our paper [arXiv]

@article{liu2022graph,
    title={Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities},
    author={Chuang Liu and Yibing Zhan and Chang Li and Bo Du and Jia Wu and Wenbin Hu and Tongliang Liu and Dacheng Tao},
    year={2022},
    journal={arXiv preprint arXiv:2204.07321},
}

• Analysis Papers

• Flat Pooling

• Hierarchical Pooling

  • Node Clustering Pooling
  • Node Drop Pooling
  • Other Pooling

• Applications

---

Analysis_Papers

1. Rethinking pooling in graph neural networks, NIPS 2020.

2. Understanding Pooling in Graph Neural Networks, ArXiv 2021.

3. A Fair Comparison of Graph Neural Networks for Graph Classification, ICLR 2020.

4. Graph Signal Processing and Deep Learning, IEEE Signal Processing Magazine 2020.

5. Revisiting Global Pooling through the Lens of Optimal Transport, ArXiv 2022.

6. Graph Neural Networks with Adaptive Readouts, NeurIPS 2022.

---

Flat_Pooling

Title	Venue	Task	Code (16/25)	Dataset
25. DMLAP: Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities	Neural Networks 2022	1. Graph Classification	None	synthetic, OGB-molhiv, OGB-ppa, MCF-7 (TU dataset)
24. GraphTrans: Representing Long-Range Context for Graph Neural Networks with Global Attention 🌟	NIPS 2021	1. Graph Classification	1.PyTorch	NCI1, NCI109, code2, molpcba
23. GMT: Accurate Learning of Graph Representations with Graph Multiset Pooling. 🌟	ICLR 2021	1. Graph Classification 2. Graph Reconstruction 3. Graph Generation	1.PyTorch 2.PyTorch-Geometric	D&D, PROTEINS, MUTAG, IMDB-B, IMDB-M, COLLAB, OGB-MOLHIV, OGB-Tox21, OGB-ToxCast, OGB-BBBP, ZINC(Reconstruction), QM9(Generation)
22. QSGCNN: Learning Graph Convolutional Networks based on Quantum Vertex Information Propagation	TKDE 2021	1. Graph Classification	None	MUTAG, PTC, NCI1, PROTEINS, D&D, COLLAB, IMDB-B, IMDB-M, RED-B
21. DropGNN: DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks	NIPS 2021	1. Graph Classification 2. Graph Regression	PyTorch	MUTAG, PTC, PROTEINS, IMDB-B, IMDB-M QM9(Regression)
20. SSRead: Learnable Structural Semantic Readout for Graph Classification	ICDM 2021	1. Graph Classification	PyTorch	D&D, MUTAG, Mutagencity, NCI1,PROTEINS, IMDB-B, IMDB-M
19. FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows	ArXiv 2021	1. Graph Classification	None	BZR, COX2, PROTEINS
18. DKEPool: Distribution Knowledge Embedding for Graph Pooling	TKDE 2022	1. Graph Classification	PyTorch	IMDB-B, IMDB-M, MUTAG, PTC, NCI1, PROTEINS, REDDIT-BINARY, OGB-MOLHIV, OGB-BBB
17. FusionPooling: Hybrid Low-order and Higher-order Graph Convolutional Networks	Computational Intelligence and Neuroscience 2020	1. Text Classification 2. node classification	None	20-Newsgroups // Cora, CiteSeer, PubMed
16. SOPool: Second-Order Pooling for Graph Neural Networks	TPAMI 2020	1. Graph Classification	None	MUTAG, PTC PROTEINS, NCI1, COLLAB, IMDB-B, IMDB-M, REDDIT-BINARY,REDDIT-MULTI,
15. StructSa: Structured self-attention architecture for graph-level representation learning	Pattern Recognition 2020	1. Graph Classification	None	MUTAG, PTC PROTEINS, NCI1, COLLAB, IMDB-B, IMDB-M, REDDIT-BINARY,REDDIT-MULTI,
14. NAS: Graph Neural Network Architecture Search for Molecular Property Prediction	ICBD 2020	1. Graph Regression	None	QM7, QM8, QM9, ESOL, FreeSolv, Lipophilicity
13. Neural Pooling for Graph Neural Networks	ArXiv 2020	1. Graph Classification	None	MUTAG, PTC PROTEINS, NCI1, COLLAB, IMDB-B, IMDB-M, REDDIT-BINARY,REDDIT-MULTI-5K,
12. GFN: Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification	ArXiv 2019	1. Graph Classification	PyTorch	MUTAG, PROTEINS, D&D, NCI1, ENZYMES, IMDB-B, IMDB-M, RDT-B. REDDTIT-Multi-5K, REDDIT-Multi-12K, COLLAB
11. GIN: How Powerful are Graph Neural Networks?	ICLR 2019	1. Graph Classification	PyTorch	MUTAG, PROTEINS, PTC, NCI1, IMDB-B, IMDB-M, RDT-B. RDT-Multi-5K, COLLAB
10. : Semi-Supervised Graph Classification: A Hierarchical Graph Perspective	WWW 2019	1. Graph Classification	PyTorch	Tencent
9. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	KDD 2019	1. Graph Classification	TensorFlow	MUTAG, PTC PROTEINS,ENZYMES
8. MSNAPool: Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity	IJCAI 2019	1. Graph Classification 2. Graph similarity ranking 3. Graph visualization	TensorFlow	PTC, IMDB-B, WEB, NCI109, REDDIT-Multi-12K
7. PiNet: Attention Pooling for Graph Classification	NIPS-W 2019	1. Graph Classification	Code	MUTAG, PTC, NCI1, NCI109, PROTEINS, Erdõs-Rényi graphs
6. DAGCN: Dual Attention Graph Convolutional Networks	IJCNN 2019	1. Graph Classification	PyTorch	NCI1, D&D, ENZYMES, NCI109, PROTEINS, PTC
5. DeepSet: Universal Readout for Graph Convolutional Neural Networks	IJCNN 2019	1. Graph Classification	Code	MUTAG, PTC, NCI1, PROTEINS,D&D
4. SortPool: An End-to-End Deep Learning Architecture for Graph Classification	AAAI 2018	1. Graph Classification	1.PyTorch-Geometric, 2.Matlab, 3.PyTorch 4.Spektral	MUTAG, PTC, NCI1 PROTEINS, D&D
3. Set2set: Order Matters: Sequence to Sequence for Sets	ICLR 2016		PyTorch-Geometric
2. GatedPool: Gated Graph Sequence Neural Networks	ICLR 2016		PyTorch-Geometric
1. DCNN: Diffusion-Convolutional Neural Networks	NIPS 2016	1. Graph Classification	Theano	NCI1, NCI109, MUTAG, PCI, ENZYMES.

---

Hierarchical_Pooling

Node_Clustering_Pooling

Title	Venue	Task	Code (14/26)	Dataset
26. Maximal Independent Vertex Set Applied to Graph Pooling	CIKM 2022	1. Graph Classification	None	PROTEINS, NCI1, D&D, ENZYMES
25. Higher-order Clustering and Pooling for Graph Neural Networks	CIKM 2022	1. Graph Classification 2. Node Clustering	1.PyTorch	PROTEINS, NCI1, D&D, MUTAGEN., Reddit-B, Cox2-MD, ER-MD, b-hard // Cora, PubMed, DBLP, Coauthor CS ,Amazon Photo, Amazon PC, Polblogs, Eu-email
24. Unsupervised Hierarchical Graph Pooling via Substructure-Sensitive Mutual Information Maximization	CIKM 2022	1. Graph Classification	None	MUTAG, PROTEINS, PTC, HIV, IMDB-B, IMDB-M
23. Structural Entropy Guided Graph Hierarchical Pooling	ICML 2022	1. Graph Classification 2. Node Classification 3. Graph Reconstruction	1. PyTorch	MUTAG, PROTEINS, D&D, PTC, NCI1,IMDB-B, IMDB-M // Cora, Citeseer, Pubmed // synthetic datasets (grid and circle)
22. HGCN:Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport	AAAI 2021	1. Graph Classification	1. PyTorch	MUTAG, PROTEINS, D&D, NCI109,IMDB-B, IMDB-M
21. Hierarchical Graph Representation Learning with Local Capsule Pooling	MMAsia 2021	1. Graph Classification 2. Graph Reconstruction	1. PyTorch	MUTAG, PROTEINS, D&D, PTC, NCI1,IMDB-B, IMDB-M //synthetic datasets (grid and circle)
20. HGCN:Hierarchical Graph Capsule Network	AAAI 2021	1. Graph Classification	1. PyTorch	MUTAG, NCI1, PROTEINS, D&D, ENZYMES, PTC, NCI109,IMDB-B, IMDB-M, Reddit-BINARY
19. HAP: Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning	TKDE 2021	1. Graph Classification 2. Graph Matching 3. Graph Similarity Learning	None	IMDB-B, IMDB-M, COLLAB, MUTAG, PROTEINS, PTC // synthetic datasets (graph matching) // AIDS, LINUX (graph similarity)
18. LCP: Hierarchical Graph Representation Learning with Local Capsule Pooling	MMAsia	1. Graph Classification 2. Graph Reconstruction	None	D&D, PROTEINS, IMDB-B, IMDB-M, NCI1, NIC109
17. MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning	ArXiv 2021	1.Graph Classification	None	D&D, ENZYMES, PROTEINS, NCI109, COLLAB, RDT-MULTI
16. HIBPool: Structure-Aware Hierarchical Graph Pooling using Information Bottleneck	IJCNN 2021	1. Graph Classification	1.PyTorch	ENZYMES, DD, PROTEINS, NCI1, NCI109,FRANKENSTEIN
15. MLC-GCN: Graph convolutional networks with multi-level coarsening for graph classification	Knowledge-Based Systems 2020	1.Graph Classification	None	D&D, ENZYMES, MUTAG, PROTEINS,IMDB-BINARY, IMDB-MULTI, REDDIT- BINARY, REDDIT-MULTI-5K
14. DGM: Deep Graph Mapper: Seeing Graphs through the Neural Lens	NIPS-W 2020	1. Graph Classification 2. Graph Visualisation	1.PyTorch	D&D, PROTEINS, COLLAB, REDDIT-B
13. MuchGNN: Multi-Channel Graph Neural Networks	IJCAI 2020	1. Graph Classification	None	PTC, DD, PROTEINS, COLLAB, IMDB-BINARY, IMDB-MULTI, REDDIT-MULTI-12K
12. MinCutPool: Spectral Clustering with Graph Neural Networks for Graph Pooling	ICML 2020	1. Graph Classification 2. Graph Regression	1.PyTorch-Geometric, 2.PyTorch	D&D, PROTEINS, COLLAB, REDDIT-BINARY, Mutagenicity, QM9(regression)
11. HaarPool: Haar Graph Pooling	ICML 2020	1. Graph Classification 2. Graph Regression	1.PyTorch	MUTAG, PROTEINS, NCI1, NCI109, MUTAGEN, TRIANGLES, QM7(regression),
10. MemPool: Memory-Based Graph Networks	ICLR 2020	1. Graph Classification 2. Graph Regression	1.PyTorch-Geometric, 2.PyTorch	D&D, PROTEINS, COLLAB, REDDIT-BINARY,ENZYMES ESOL(reg), Lipophilicity(reg)
9. StructPool: Structured Graph Pooling via Conditional Random Fields	ICLR 2020	1.Graph Classification	1. PyTorch	ENZYMES, PTC, MUTAG, PROTEINS, COLLAB, IMDB-B, IMDB-M
8. MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning	ArXiv 2020	1.Graph Classification 2. Graph Regression	None	D&D, PROTEINS, MUTAG, ENZYMES // QM7 (regression) MUTA-GENICITY
7.ProxPool: Graph Pooling with Node Proximity for Hierarchical Representation Learning	ArXiv 2020	1.Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, MUTA-GENICITY
6. CliquePool: Clique pooling for graph classification	ICLR-W 2019	1. Graph Classification	None	D&D PROTEINS, ENZYMES, COLLAB
5. NMF: A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks	AIIA 2019	1. Graph Classification	None	D&D, PROTEINS, NCI1, ENZYMES, COLLAB
4. GRAHIES: Multi-Scale Graph Representation Learning with Latent Hierarchical Structure	CogMI 2019	1. Node Classification	None	Cora, CiteSeer, PubMed
3. EigenPool: Graph Convolutional Networks with EigenPooling	KDD 2019	1. Graph Classification	1.PyTorch	D&D, PROTEINS, NCI1, NCI109, MUTAG, ENZYMES
2. LaPool : Towards Interpretable Molecular Graph Representation Learning	ArXiv 2019	1. Graph Classification 2. Graph Regression 3. Molecular Generation	1.Pytorch	TOX21, D&D, PROTEINS, FRANKENSTEIN, QM9(regression)
1. DiffPool: Hierarchical Graph Representation Learning with Differentiable Pooling	NIPS 2018	1. Graph Classification	1.PyTorch-Geometric, 2.PyTorch	D&D, PROTEINS, COLLAB, ENZYMES, REDDIT-MULTI

---

Node_Drop_Pooling

Title	Venue	Task	Code (16/28)	Dataset
28. Multi-grained Semantics-aware Graph Neural Networks	TKDE 2022	1. Graph Classification 2. Node Classifcation 3. Link Prediction	1.PyTorch	D&D, PROTEINS, NCI1, NCI109, MUTAG,Mutagenicity // Ogbn-arxiv , ACM , Cora, Citeseer, Pubmed ,DBLP, Emails, Wiki //ACM , Cora, Citeseer, Pubmed ,DBLP, Emails, Wiki
27. Graph Pooling in Graph Neural Networks with Node Feature Correlation	DSIT 2022	1. Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, MUTAG
26. Node Information Awareness Pooling for Graph Representation Learning	PAKDD 2022	1. Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, FRANKEN
25. LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning	ArXiv 2022	1. Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, FRANKEN
24. NCPool: Seeing All From a Few: Nodes Selection Using Graph Pooling for Graph Clustering	ArXiv 2021	1. Node Clustering	None	Cora, CiteSeer, PubMed, DBLP, ACM
23. MEWISPool: Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks	ArXiv 2021	1. Graph Classification	1. PyTroch	D&D, PROTEINS, MUTAG, FRANKENSTEIN, MUTAGENICITY, IMDB-BINARY, IMDB-MULTI, COLLAB,
22. EGC2: Enhanced Graph Classification with Easy Graph Compression	ArXiv 2021	1. Graph Classification	None	D&D, PTC, NCI1, NCI109, IMDB-BINARY, REDDIT-BINARY,
21. TAPPool: Topology-Aware Graph Pooling Networks	TPAMI 2021	1. Graph Classification	None	D&D, PTC, MUTAG, COLLAB, REDDIT-MULTI,
20. MVPool: Hierarchical Multi-View Graph Pooling with Structure Learning	TKDE 2021	1. Graph Classification 2. Graph Clustering 3. Node Classification 4. Node Clustering	1. PyTorch	D&D, PROTEINS, ENZYMES, NCI1, MCI109 // Cora, CiteSeer, PubMed, Coauthor-CS, Coauthor-Physics
19. Ipool: Information-Based Pooling in Hierarchical Graph Neural Networks	TNNLS 2021	1. Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, ENZYMES, MNIST, CIFAR-10
18. CGIPool: Graph Pooling via Coarsened Graph Infomax	SIGIR 2021	1. Graph Classification	1.PyTorch	NCI1, NCI109, Mutagenicity, IMDB-B, IMDB-M, COLLAB PROTEINS
17. CommPOOL:An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning	Neural Networks 2021	1. Graph Classification	None	BZR, FRANKENSTEIN, PROTEINS, AIDS, Synthie
16. PGP: Learning Hierarchical Review Graph Representations for Recommendation	ArXiv 2021	1. Recommandation	None	Amazon
15. NDP: Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling	TNNLS 2020	1. Graph Classification	1. TensoFlow	PROTEINS, ENZYMES, NCI1, MUTAG, Mutagenicity, D&D,COLLAB, Reddit-Binary
14. VIPool: Graph Cross Networks with Vertex Infomax Pooling	NIPS 2020	1. Graph Classification 2. Node Classification	1.PyTorch	IMDB- B, IMDB-M, COLLAB, D&D, PROTEINS, ENZYMES
13. HGP-SL: Hierarchical Graph Pooling with Structure Learning	AAAI 2020	1. Graph Classification	1.PyTorch	D&D, PROTEINS, NCI1, NCI109, ENZYMES, Mutagencity
12. GSAPool: Structure-Feature based Graph Self-adaptive Pooling	WWW 2020	1. Graph Classification	1.Pytorch	D&D, NCI1, NCI109, Mutagencity
11. PANPool: Path Integral Based Convolution and Pooling for Graph Neural Networks	NIPS 2020	1. Graph Classification	1.PyTorch-Geometric	PROTEINS, PROTEINS-FULL, NCI1, AIDS, MUTAGENCITY
10. UGPool: Uniform Pooling for Graph Networks	Appl. Sci. 2020	1. Graph Classification	1.PyTorch	D&D, PROTEINS, NCI1, NCI109, ENZYMES, HCP(Brain), ABIDE(Brain)
9. ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations	AAAI 2020	1. Graph Classification	1.PyTorch-Geometric 2.PyTorch	D&D, PROTEINS, NCI1, NCI109, FRANKENSTEIN
8. BUTDPool: Bottom-Up and Top-Down Graph Pooling	PAKDD 2020	1. Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, REDDIT-Binary
7. LookHops: light multi-order convolution and pooling for graph classification	ArXiv 2020	1. Graph Classification	None	PROTEINS, D&D, NCI1, NCI109, Mutagenicity, FRANKENSTEIN
6. RepPool:Graph Pooling with Representativeness	ICDM 2020	1. Graph Classification	1.PyTorch	PROTEINS, D&D, NCI1, NCI109, MUTAG, PTC, IMDB-BINARY, Synthie
5. AttPool : Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism	ICCV 2019	1. Graph Classification	1.PyTorch	D&D, PROTEINS, COLLAB, REDDIT-BINARY,REDDIT-MULTI, NCI1
4. SAGPool: Self-Attention Graph Pooling	ICML 2019	1. Graph Classification	1.PyTorch-Geometric 2.Spekral 2.PyTorch	D&D, PROTEINS, NCI1, NCI109, FRANKENSTEIN
3. TopKPool: Understanding Attention and Generalization in Graph Neural Networks	ICLR-W 2019 NIPS 2019	1. Graph Classification	1.PyTorch-Geometric 2.Spektral 3.PyTorch	D&D, PROTEINS, COLLAB, CCOLORS, TRIANGLES, MNIST-75sp
2. TopKPool: Graph U-Nets	ICML 2019,	1. Graph Classification 2. Node Classification	1.PyTorch-Geometric 2.Spektral 3.PyTorch	D&D, PROTEINS, COLLAB // Cora, CiteSeer, PubMed
1. TopKPool: Towards Sparse Hierarchical Graph Classifiers	NIPS-W 2018	1. Graph Classification	1.PyTorch-Geometric 2.Spektral	D&D, PROTEINS, COLLAB, ENZYMES

---

Other_Pooling

Title	Venue	Note	Task	Code (7/20)	Dataset
20. An Unpooling Layer for Graph Generation	ArXIv 2022	Unpooling	1. Graph Generation	None	QM9, ZINC
19. Diversified Multiscale Graph Learning with Graph Self-Correction	ArXIv 2021	Self-Correction	1. Graph Classifcation	None	D&D, PROTEINS, NCI1, NCI109, FRANKENSTEIN, OGB-MOLHIV
18. Pyramidal Reservoir Graph Neural Network	Neurocomputing 2021	Clique pooling	1. Graph Classifcation	1.Spektral	D&D, PROTEINS, NCI1, ENZYMES, MUTANGENICITY, IMDB-BINARY, IMDB-MULTI, COLLAB, REDDIT-Binary, REDDIT-Multi-5K
17. KPlexPool: K-plex cover pooling for graph neural networks	PKDD 2021	Clique pooling	1. Graph Classifcation	1.PyTorch	D&D, PROTEINS,IMDB-BINARY, IMDB-MULTI, COLLAB, REDDIT-Binary, REDDIT-Multi-5K
16. DHT: Edge Representation Learning with Hypergraphs	NIPS 2021	HyperGraph	1. Graph Classifcation 2. Graph generation 3. Graph reconstruction 4. Node Classification	1.PyTorch	D&D, PROTEINS, MUTAG,IMDB-BINARY, IMDB-MULTI, COLLAB,HIV, Tox21, ToxCast, BBBP // ZINC (regression and generation) // Cora, CiteSeer (node classification)
15. MuchPool: Multi-Channel Pooling Graph Neural Networks	IJCAI 2021	Node Drop + Node Clustering	1. Graph Classification	None	D&D, PROTEINS, NCI1, NCI109, ENZYMES, COLLAB
14. Co-Pooling: Edge but not Least: Cross-View Graph Pooling	ArXiv 2021	Edge View	1. Graph Classification 2. Graph Regression	Node	D&D, PROTEINS, NCI1, NCI109, MSRC_21, COLLAB, IMDB-B, IMDB-M, REDDIT-Binary, REDDIT-Multi-12K FRANKENSTEIN, AIDS, BZR // ZINC, QM9(regression)
13. GNP: Learning to Pool in Graph Neural Networks for Extrapolation	ArXiv 2021	Extrapolation		1. PyTroch
12. EdgeCut:Graph Pooling by Edge Cut	ArXiv 2021	Edge View	1. Graph Classification 2. Node Classification	1. PyTorch	D&D, PROTEINS, COLLAB Cora, CiteSeer, PubMed
11. Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results	ArXiv 2021	Edge View	1. Count Substructure	1. PyTorch	Erdo ̋s-Renyi
10. PAS: Pooling Architecture Search for Graph Classification	CIKM 2021	Neuarl Architecher Search	1.Graph Classification	None	D&D, PROTEINS, IMDB-B, IMDB-M, COX21
9. Adversarial Attack on Hierarchical Graph Pooling Neural Networks	ArXiv 2020	Attack	1. Graph Classification	None	D&D, Mutagenicity, AIDS, BZR, DHFR, ER_MD
8. Robust Hierarchical Graph Classification with Subgraph Attention	ArXiv 2020	SUbgraph Attention	1. Graph Classification 2. Graph Clustering	None	MUTAG, PTC, PROTEINS, NCI1, NCI109, IMDB-B, IMDB-M // MUTAG, PROTEINS, IMDB-M
7. Graphon Pooling in Graph Neural Networks	ArXiv 2020	Graphon	None	None	None
6. A Multi-Task Representation Learning Architecture for Enhanced Graph Classification	Neroscience 2020	Multi-task	1 Graph Classification 2. Node Classification	None	MUTAG, PTC, ENZYMES, PROTEINS, NCI1
5. LRP-Pool: Can Graph Neural Networks Count Substructures?	NIPS 2020	WL-test	1. Graph Classification	1. PyTorch	HIV, ZINC, QM9
4. RP-Pool: Relational Pooling for Graph Representations	ICML 2019	WL-test	1. Graph Classification	1. PyTorch	HIV, MUV, Tox21
3. Multi-Task Learning on Graphs with Node and Graph Level Labels	NIPS-W 2019	Multi-task	1. Graph Classification 2. Node Classification	None	ENZYMES, PROTEINS
2. EdgePool: Towards Graph Pooling by Edge Contraction	ICML-W 2019	Edge view	1. Graph Classification	1.PyTorch-Geometric	PROTEINS, COLLAB, REDDIT-BINARY, REDDIT-MULTI
1. Graph Analysis and Graph Pooling in the Spatial Domain	ArXiv 2019	Analysis	1. Graph Classifcation	None	D&D, PROTEINS, ENZYMES, MDC, CNC, HLLD, SYNTHIETIC

---

Applications

Title	Venue	Application	Code (20/45)
45. Learning Hierarchical Graph Convolutional Neural Network for Object Navigation	ICANN 2022	Object navigation in AI2-iTHOR	None
44. Relation Prediction Based on Source-Entity Behavior Preference Modeling via Heterogeneous Graph Pooling	KSEM 2022	Relation Prediction in Knowledge Graph	None
43. Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling	ArXiv 2022	Fake news detection, Programme Problem classification	None
42. HiGIL: Hierarchical Graph Inference Learning for Fact Checking	ICDM 2022	Fact Checking (NLP)	None
41. Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction	ArXiv 2022	Extreme Molecular Property Prediction	None
40. Compositionality-Aware Graph2Seq Learning	ArXiv 2022	Extreme source code summarization	code
39. Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels	ArXiv 2022	Image Classification	None
38. ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection	ArXiv 2022	Vulnerability Detection	1. PyTorch
37. End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection	ArXiv 2021	Speaker Verification Anti-Spoofing and Speech Deepfake Detection	code
36. Graph attentive feature aggregation for text-independent speaker verification	ArXiv 2021	Speaker Verification	None
35. Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation	ArXiv 2021	Recommendation	None
34. Tree-Constrained Graph Neural Networks For Argument Mining	ArXiv 2021	Argument Mining	1. TensorFlow
33. EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals	ArXiv 2021	Electroencephalogram (EEG) Signals	None
32. Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning	ArXiv 2021	Brain data (fMRI)	PyTorch
31. Dynamic Emotion Modeling with Learnable Graphs and Graph Inception Network	IEEE TMM	Emotion Recognition	1. PyTorch
30. Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures	ICLR 2021	3D Protein Structures	code
29. Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention	NIPS 2021	Brain Connectome	code
28. Interpretable Graph Capsule Networks for Object Recognition	AAAI 2021	Object detection	None
27. GDPNet: Refining Latent Multi-View Graph for Relation Extraction	AAAI 2021	Relation Extraction	1. PyTorch
26. User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation	IJCAI 2021	Recommendation	None
25. Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity	KDD 2021	Protein binding	1. Paddle
24. Sequential Recommendation with Graph Neural Networks	SIGIR 2021	Recommendation	None
23. KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction	ACL 2021	Knowledge Graph	1. PyTroch
22. Learnable Pooling in Graph Convolutional Networks for Brain Surface Analysis	TPAMI 2021	Brain Surface	1. PyTorch
21. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis	Medical Image Analysis 2021	Brain data (fMRI) analysis	1. PyTroch
20. PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds	ArXiv 2021	3D Point Cloud	None
19. Discourse-level Relation Extraction via Graph Pooling	ArXiv 2021	NLP	None
18. Learning Hierarchical Review Graph Representations for Recommendation	ArXiv 2021	Recommendation	None
17. Multivariate Time Series Classification with Hierarchical Variational Graph Pooling	ArXiv 2021	Time Series Classification	None
16. HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation	IEEE TMM 2020	Point Cloud	None
15. HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation	CVPR 2020	Pose Estimation	Code
14. Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis	MICCAI 2020	fMRI	None
13. GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes	MLMI 2020 Workshop	Brain	Code
12. Representation Learning of Histopathology Images using Graph Neural Networks	CVPR 2020 workshop	Histopathology Images	None
11. Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph Pooling Fusion	ArXiv 2020	Multimodal sequence	None
10. Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare	ArXiv 2019	Healthcare	None
9. ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling	ArXiv 2019	spatio-temporal prediction tasks	None
8. RepGN:Object Detection with Relational Proposal Graph Network	ArXiv 2019	Object Detection	None
7. Adaptive Graph Convolution Pooling for Brain Surface Analysis	IPMI 2019	Brain Surface	None
6. Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations	WWW 2019	NLP	1. Pytorch
5. Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling	CVPR 2018	3D Point Cloud	1. Code
4. SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels	CVPR 2018	Computer Vision	1. PyTorch Geometri
3. Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification	IJCAI 2018	Breast Cancer	None
2. MoNet: Geometric deep learning on graphs and manifolds using mixture model cnns	CVPR 2017	1.Image 2. Graph, 3. Manifolds	PyTorch-Geometric
1. ECC: Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	CVPR 2017	Point Cloud	PyTorch-Geometric