/graph-pooling-papers

IJCAI‘23 Survey Track: Papers on Graph Pooling (GNN-Pooling)

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

  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