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GNNs and related works list

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A list for GNNs and related works.

List for GNNs

Spectral domain GNNs

Convolutional GNNs

Based on graph Fourier transform
Number GNN Paper Code Journal or Conference URL
1 Spectral CNN Spectral Networks and Locally Connected Networks on Graphs ICLR 2014 https://openreview.net/forum?id=DQNsQf-UsoDBa
2 Deep Convolutional Networks on Graph-Structured Data https://arxiv.org/abs/1506.05163
3 ChebyNet Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering https://github.com/mdeff/cnn_graph NeurIPS 2016 https://proceedings.neurips.cc/paper/2016/hash/04df4d434d481c5bb723be1b6df1ee65-Abstract.html
4 GCN Semi-Supervised Classification with Graph Convolutional Networks https://github.com/tkipf/gcn ICLR 2017 https://openreview.net/forum?id=SJU4ayYgl
5 SGC Simplifying Graph Convolutional Networks https://github.com/Tiiiger/SGC ICML 2019 http://proceedings.mlr.press/v97/wu19e.html
6 gfNN Revisiting Graph Neural Networks: All We Have is Low-Pass Filters https://github.com/gear/gfnn https://arxiv.org/abs/1905.09550
7 CayleyNet CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters IEEE Transactions on Signal Processing https://github.com/amoliu/CayleyNet
8 MotifNet MotifNet: a motif-based Graph Convolutional Network for directed graphs 2018 IEEE Data Science Workshop https://ieeexplore.ieee.org/abstract/document/8439897
9 LanczosNet LanczosNet: Multi-Scale Deep Graph Convolutional Networks https://github.com/lrjconan/LanczosNetwork ICLR 2019 https://openreview.net/forum?id=BkedznAqKQ
10 PPNP & APPNP Predict then Propagate: Graph Neural Networks meet Personalized PageRank https://github.com/klicperajo/ppnp ICLR 2019 https://openreview.net/forum?id=H1gL-2A9Ym
11 GDC Diffusion Improves Graph Learning https://github.com/klicperajo/gdc NIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/23c894276a2c5a16470e6a31f4618d73-Abstract.html
12 GCNII Simple and Deep Graph Convolutional Networks https://github.com/chennnM/GCNII ICML 2020 https://proceedings.mlr.press/v119/chen20v.html
13 ARMA GNN Graph Neural Networks with convolutional ARMA filters https://github.com/danielegrattarola/spektral/blob/master/spektral/layers/convolutional/arma_conv.py#L10 IEEE Transactions on Pattern Analysis and Machine Intelligence https://ieeexplore.ieee.org/abstract/document/9336270
14 DFNet DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters https://github.com/wokas36/DFNets NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/f87522788a2be2d171666752f97ddebb-Abstract.html
15 FisherGCN Fisher-Bures Adversary Graph Convolutional Networks https://github.com/D61-IA/FisherGCN ICML 2020 http://proceedings.mlr.press/v115/sun20a.html
16 DGCN Spectral-based Graph Convolutional Network for Directed Graphs https://arxiv.org/abs/1907.08990
17 SIGN SIGN: Scalable Inception Graph Neural Networks ICML 2020 Workshop https://arxiv.org/abs/2004.11198
18 DiGCN Digraph Inception Convolutional Networks https://github.com/flyingtango/DiGCN NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/cffb6e2288a630c2a787a64ccc67097c-Abstract.html
19 HKGCN Generalizing Graph Convolutional Networks via Heat Kernel https://openreview.net/forum?id=yBJihVXahXc
20 S2GC Simple Spectral Graph Convolution https://github.com/allenhaozhu/SSGC ICLR 2021 https://openreview.net/forum?id=CYO5T-YjWZV
21 GPR-GNN Adaptive Universal Generalized PageRank Graph Neural Network https://github.com/jianhao2016/GPRGNN ICLR 2021 https://openreview.net/forum?id=n6jl7fLxrP
22 GA-MLP On Graph Neural Networks versus Graph-Augmented MLPs https://github.com/leichen2018/GNN_vs_GAMLP ICLR 2021 https://openreview.net/forum?id=tiqI7w64JG2
23 MagNet MagNet: A Neural Network for Directed Graphs https://github.com/matthew-hirn/magnet NeurIPS 2021 https://openreview.net/forum?id=TRDAFiwDq8A
24 BernNet BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation https://github.com/ivam-he/BernNet NeurIPS 2021 https://openreview.net/forum?id=WigDnV-_Gq
25 EdgeNet EdgeNets: Edge Varying Graph Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence https://ieeexplore.ieee.org/abstract/document/9536420
26 AdaGNN AdaGNN: Graph Neural Networks with Adaptive Frequency Response https://github.com/yushundong/AdaGNN CIKM 2021 https://dl.acm.org/doi/abs/10.1145/3459637.3482226
27 ADA-UGNN A Unified View on Graph Neural Networks as Graph Signal Denoising https://github.com/alge24/ADA-UGNN CIKM 2021 https://dl.acm.org/doi/abs/10.1145/3459637.3482225
28 AirGNN Graph Neural Networks with Adaptive Residual https://github.com/lxiaorui/AirGNN NeurIPS 2021 https://proceedings.neurips.cc/paper/2021/hash/50abc3e730e36b387ca8e02c26dc0a22-Abstract.html
29 ADC Adaptive Diffusion in Graph Neural Networks https://github.com/abcbdf/ADC NeurIPS 2021 https://proceedings.neurips.cc/paper/2021/hash/c42af2fa7356818e0389593714f59b52-Abstract.html
30 U-GCN Universal Graph Convolutional Networks https://github.com/jindi-tju/U-GCN NeurIPS 2021 https://papers.nips.cc/paper/2021/hash/5857d68cd9280bc98d079fa912fd6740-Abstract.html
31 pGNN p-Laplacian Based Graph Neural Networks https://openreview.net/forum?id=i8d2kdxii1L
Based on graph Wavelet transform
Number GNN Paper Code Journal or Conference URL
1 GraphWave Learning Structural Node Embeddings via Diffusion Wavelets https://github.com/benedekrozemberczki/GraphWaveMachine KDD 2018 https://www-cs.stanford.edu/~jure/pubs/graphwave-kdd18.pdf
2 GWNN Graph Wavelet Neural Network https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork ICLR 2019 https://openreview.net/forum?id=H1ewdiR5tQ
3 HANet Fast Haar Transforms for Graph Neural Networks Neural Networks https://www.sciencedirect.com/science/article/abs/pii/S0893608020301568
4 MathNet MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning https://arxiv.org/abs/2007.11202
5 UFGConv and UFGPool How Framelets Enhance Graph Neural Networks https://github.com/YuGuangWang/UFG ICML 2021 http://proceedings.mlr.press/v139/zheng21c.html

Graph Scattering Transforms

Number GNN or method Paper Code Journal or Conference URL
1 Diffusion Scattering Transforms on Graphs ICLR 2019 https://openreview.net/forum?id=BygqBiRcFQ
2 Stability of Graph Scattering Transforms https://github.com/alelab-upenn/graph-scattering-transforms NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/3ce3bd7d63a2c9c81983cc8e9bd02ae5-Abstract.html
3 Geometric Scattering for Graph Data Analysis ICML 2019 http://proceedings.mlr.press/v97/gao19e.html
4 Graph convolutional neural networks via scattering https://github.com/dmzou/SCAT Applied and Computational Harmonic Analysis https://www.sciencedirect.com/science/article/abs/pii/S1063520318300678
5 Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds ICML 2020 https://proceedings.mlr.press/v107/perlmutter20a.html
5 Data-Driven Learning of Geometric Scattering Networks https://arxiv.org/abs/2010.02415
6 Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms https://arxiv.org/abs/1911.06253
7 Scattering GCN Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks https://github.com/dms-net/scatteringGCN NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/a6b964c0bb675116a15ef1325b01ff45-Abstract.html
8 GSAN Geometric Scattering Attention Networks https://github.com/dms-net/Attention-based-Scattering ICASSP https://ieeexplore.ieee.org/abstract/document/9414557/

Bayesian GNN

Number GNN Paper Code Journal or Conference URL
1 Bayesian GCN Bayesian graph convolutional neural networks for semi-supervised classification https://github.com/huawei-noah/BGCN AAAI 2019 https://ojs.aaai.org//index.php/AAAI/article/view/4531
2 GPN Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification https://github.com/stadlmax/Graph-Posterior-Network NeurIPS 2021 https://papers.nips.cc/paper/2021/hash/95b431e51fc53692913da5263c214162-Abstract.html

Attentional GNNs

Number GNN Paper Code Journal or Conference URL
1 SpGAT Spectral Graph Attention Network with Fast Eigen-approximation https://github.com/SwiftieH/SpGAT CIKM 2021 https://dl.acm.org/doi/abs/10.1145/3459637.3482187

Graph Pooling (Graph Coarsening)

Number Graph Pooling Paper Code Journal or Conference URL
1 LaPool Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling https://arxiv.org/abs/1905.11577
2 EigenPooling Graph Convolutional Networks with EigenPooling https://github.com/alge24/eigenpooling KDD 2019 https://dl.acm.org/doi/10.1145/3292500.3330982
3 HaarPool Haar Graph Pooling https://github.com/YuGuangWang/HaarPool ICML 2020 http://proceedings.mlr.press/v119/wang20m.html

Spatial/Vertex domain GNNs

Convolutional GNNs

Number GNN Paper Code Journal or Conference URL
1 DCNN Diffusion-Convolutional Neural Networks https://github.com/RicardoZiTseng/dcnn-tensorflow NeurIPS 2016 https://proceedings.neurips.cc/paper/2016/hash/390e982518a50e280d8e2b535462ec1f-Abstract.html
2 GraphSAGE Inductive Representation Learning on Large Graphs https://github.com/williamleif/GraphSAGE NeurIPS 2017 https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html
3 FastGCN FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling https://github.com/matenure/FastGCN ICLR 2018 https://openreview.net/forum?id=rytstxWAW
4 JK-Net Representation Learning on Graphs with Jumping Knowledge Networks ICML 2018 http://proceedings.mlr.press/v80/xu18c.html
5 GIN How Powerful are Graph Neural Networks? https://github.com/weihua916/powerful-gnns ICLR 2019 https://openreview.net/forum?id=ryGs6iA5Km
6 k-GNN Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks https://github.com/chrsmrrs/k-gnn AAAI 2019 https://ojs.aaai.org/index.php/AAAI/article/view/4384
7 Cluster-GCN Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks https://github.com/google-research/google-research/tree/master/cluster_gcn KDD 2019 https://dl.acm.org/doi/abs/10.1145/3292500.3330925
8 Geom-GCN Geom-GCN: Geometric Graph Convolutional Networks https://github.com/graphdml-uiuc-jlu/geom-gcn ICLR 2020 https://openreview.net/forum?id=S1e2agrFvS
9 DAGNN Towards Deeper Graph Neural Networks https://github.com/divelab/DeeperGNN KDD 2020 https://dl.acm.org/doi/abs/10.1145/3394486.3403076
10 H2GCN Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs https://github.com/GemsLab/H2GCN NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/58ae23d878a47004366189884c2f8440-Abstract.html
11 DGN Directional Graph Networks https://github.com/Saro00/DGN ICML 2021 http://proceedings.mlr.press/v139/beani21a.html
12 Elastic GNN Elastic Graph Neural Networks https://github.com/lxiaorui/ElasticGNN ICML 2021 https://proceedings.mlr.press/v139/liu21k.html
13 SIN Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks https://github.com/twitter-research/cwn ICML 2021 https://proceedings.mlr.press/v139/bodnar21a.html
14 CIN Weisfeiler and Lehman Go Cellular: CW Networks https://github.com/twitter-research/cwn NeurIPS 2021 https://openreview.net/forum?id=uVPZCMVtsSG
15 VQ-GNN VQ-GNN: A Universal Framework to Scale-up Graph Neural Networks using Vector Quantization https://github.com/devnkong/VQ-GNN NeurIPS 2021 https://openreview.net/forum?id=EO-CQzgcIxd
16 GraphSNN A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" https://openreview.net/forum?id=uxgg9o7bI_3

Attentional GNNs

Number GNN Paper Code Journal or Conference URL
1 AGNN Attention-based Graph Neural Network for Semi-supervised Learning https://github.com/dawnranger/pytorch-AGNN https://arxiv.org/abs/1803.03735
2 GAT Graph Attention Network https://github.com/PetarV-/GAT ICLR 2018 https://openreview.net/forum?id=rJXMpikCZ
3 MCN Higher-order Graph Convolutional Networks
4 CS-GNN Measuring and Improving the Use of Graph Information in Graph Neural Networks https://github.com/yifan-h/CS-GNN ICLR 2020 https://openreview.net/forum?id=rkeIIkHKvS
5 MixHop MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing https://github.com/samihaija/mixhop ICML 2019 http://proceedings.mlr.press/v97/abu-el-haija19a.html
6 GaAN GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs https://github.com/jennyzhang0215/GaAN UAI 2018 http://www.auai.org/uai2018/proceedings/papers/139.pdf
7 GAM Graph Classification using Structural Attention https://github.com/benedekrozemberczki/GAM KDD 2018 https://dl.acm.org/doi/abs/10.1145/3219819.3219980
8 hGANet Graph Representation Learning via Hard and Channel-Wise Attention Networks KDD 2019 https://dl.acm.org/doi/abs/10.1145/3292500.3330897
9 RGCN and RGAT Relational Graph Attention Networks https://github.com/babylonhealth/rgat https://arxiv.org/abs/1904.05811
10 C-GAT Improving Graph Attention Networks with Large Margin-based Constraints NeurIPS 2019 Workshop https://grlearning.github.io/papers/43.pdf
11 FAGCN Beyond Low-frequency Information in Graph Convolutional Networks https://github.com/bdy9527/FAGCN AAAI 2021 https://ojs.aaai.org/index.php/AAAI/article/view/16514
12 CAT-I and CAT-E Learning Conjoint Attentions for Graph Neural Nets ICLR 2021 https://openreview.net/forum?id=SMU_hbhhEQ
13 DPGAT and GATv2 How Attentive are Graph Attention Networks? https://github.com/tech-srl/how_attentive_are_gats
14 GAMLP Graph Attention Multi-Layer Perceptron https://github.com/pku-dair/gamlp

Graph Pooling (Graph Coarsening)

Number Graph Pooling Paper Code Journal or Conference URL
1 SortPooling An End-to-End Deep Learning Architecture for Graph Classification https://github.com/muhanzhang/DGCNN AAAI 2018 https://ojs.aaai.org/index.php/AAAI/article/view/11782
2 DiffPool Hierarchical Graph Representation Learning with Differentiable Pooling https://github.com/RexYing/diffpool NeurIPS 2018 https://proceedings.neurips.cc/paper/2018/hash/e77dbaf6759253c7c6d0efc5690369c7-Abstract.html
3 gPool and gUnpool Graph U-Nets https://github.com/HongyangGao/Graph-U-Nets ICML 2019 http://proceedings.mlr.press/v97/gao19a.html
4 SAGPool Self-Attention Graph Pooling https://github.com/inyeoplee77/SAGPool ICML 2019 https://proceedings.mlr.press/v97/lee19c.html
5 Relational Pooling Relational Pooling for Graph Representations https://github.com/PurdueMINDS/RelationalPooling ICML 2019 http://proceedings.mlr.press/v97/murphy19a.html
6 HPL-SL Hierarchical Graph Pooling with Structure Learning https://github.com/cszhangzhen/HGP-SL https://arxiv.org/abs/1911.05954
7 StructPool StructPool: Structured Graph Pooling via Conditional Random Fields https://github.com/Nate1874/StructPool ICLR 2020 https://openreview.net/forum?id=BJxg_hVtwH
8 MinCutPool Spectral Clustering with Graph Neural Networks for Graph Pooling https://github.com/FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling ICML 2020 https://proceedings.mlr.press/v119/bianchi20a.html
9 GSAPool Structure-Feature based Graph Self-adaptive Pooling WWW 2020 https://dl.acm.org/doi/10.1145/3366423.3380083
10 NDP Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling https://github.com/danielegrattarola/decimation-pooling IEEE Transactions on Neural Networks and Learning Systems https://ieeexplore.ieee.org/abstract/document/9311759
11 MxPool MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning https://github.com/JucatL/MxPool/ https://arxiv.org/abs/2004.06846
12 VIPool Graph Cross Networks with Vertex Infomax Pooling https://github.com/limaosen0/GXN NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/a26398dca6f47b49876cbaffbc9954f9-Abstract.html
13 GMT Accurate Learning of Graph Representations with Graph Multiset Pooling https://github.com/JinheonBaek/GMT ICLR 2021 https://openreview.net/forum?id=JHcqXGaqiGn
14 iPool iPool—Information-Based Pooling in Hierarchical Graph Neural Networks IEEE Transactions on Neural Networks and Learning Systems https://ieeexplore.ieee.org/document/9392315

MPNNs

Number MPNN Paper Code Journal or Conference URL
1 MPNN Neural Message Passing for Quantum Chemistry https://github.com/brain-research/mpnn ICML 2017 https://proceedings.mlr.press/v70/gilmer17a.html
2 SMP Building powerful and equivariant graph neural networks with structural message-passing https://github.com/cvignac/SMP NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/a32d7eeaae19821fd9ce317f3ce952a7-Abstract.html
3 PNA Principal Neighbourhood Aggregation for Graph Nets https://github.com/lukecavabarrett/pna NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/99cad265a1768cc2dd013f0e740300ae-Abstract.html
4 EGC-S and EGC-M Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions https://github.com/shyam196/egc https://arxiv.org/abs/2104.01481

Heterogeneous Graph Neural Networks

Number HGNN Paper Code Journal or Conference URL
1 HetGNN Heterogeneous Graph Neural Network https://github.com/chuxuzhang/KDD2019_HetGNN KDD 2019 https://dl.acm.org/doi/10.1145/3292500.3330961
2 HAN Heterogeneous Graph Attention Network https://github.com/Jhy1993/HAN WWW 2019 https://dl.acm.org/doi/10.1145/3292500.3330961
3 MAGNN MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding https://github.com/cynricfu/MAGNN WWW 2020 https://dl.acm.org/doi/10.1145/3366423.3380297

Hyperbolic Graph Neural Networks

Number HGNN or method Paper Code Journal or Conference URL
1 HGCN Hyperbolic Graph Convolutional Neural Networks https://github.com/HazyResearch/hgcn NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/0415740eaa4d9decbc8da001d3fd805f-Abstract.html
2 HGNN Hyperbolic Graph Neural Networks https://github.com/facebookresearch/hgnn NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/103303dd56a731e377d01f6a37badae3-Abstract.html
3 HAT Hyperbolic Graph Attention Network ICLR 2019 https://openreview.net/forum?id=rJxHsjRqFQ
4 k-GCN Constant Curvature Graph Convolutional Networks ICML 2020 https://proceedings.mlr.press/v119/bachmann20a.html
5 HNN + HBN Differentiating through the Fréchet Mean https://github.com/CUAI/Differentiable-Frechet-Mean ICML 2020 http://proceedings.mlr.press/v119/lou20a.html
6 LGCN Lorentzian Graph Convolutional Networks WWW 2021 https://dl.acm.org/doi/abs/10.1145/3442381.3449872
7 H2H-GCN A Hyperbolic-to-Hyperbolic Graph Convolutional Network CVPR 2021 https://openaccess.thecvf.com/content/CVPR2021/html/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.html
8 HGCF HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering https://github.com/layer6ai-labs/HGCF WWW 2021 https://dl.acm.org/doi/10.1145/3442381.3450101
9 HVGNN Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs AAAI 2021 https://ojs.aaai.org/index.php/AAAI/article/view/16563
10 ACE-HGNN ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network https://github.com/ringbdstack/ace-hgnn https://arxiv.org/abs/2110.07888

Capsule Graph Neural Network

Number CGNN or method Paper Code Journal or Conference URL
1 GCAPS-CNN Graph Capsule Convolutional Neural Networks https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks/ https://arxiv.org/abs/1805.08090
2 CapsGNN Capsule Graph Neural Network https://github.com/benedekrozemberczki/CapsGNN ICLR 2019 https://openreview.net/forum?id=Byl8BnRcYm
3 NCGNN NCGNN: Node-level Capsule Graph Neural Network https://arxiv.org/abs/2012.03476
4 HGCN Hierarchical Graph Capsule Network https://github.com/uta-smile/HGCN AAAI 2021 https://ojs.aaai.org/index.php/AAAI/article/view/17268

Graph Neural ODE or PDE

Number GNODE or GNPDE or method Paper Code Journal or Conference URL
1 Graph Neural Ordinary Differential Equations https://github.com/Zymrael/gde https://arxiv.org/abs/1911.07532
2 Ordinary differential equations on graph networks https://openreview.net/forum?id=SJg9z6VFDr
3 CGF Continuous Graph Flow https://arxiv.org/abs/1908.02436
4 CGNN Continuous Graph Neural Networks https://github.com/DeepGraphLearning/ContinuousGNN ICML 2020 https://proceedings.mlr.press/v119/xhonneux20a.html
5 NDCN Neural Dynamics on Complex Networks https://github.com/calvin-zcx/ndcn KDD 2020 https://dl.acm.org/doi/abs/10.1145/3394486.3403132
6 DeltaGN and OGN Hamiltonian Graph Networks with ODE Integrators NeurIPS 2019 Workshop https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_30.pdf
7 CFD-GCN Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction https://github.com/locuslab/cfd-gcn ICML 2020 https://proceedings.mlr.press/v119/de-avila-belbute-peres20a.html
8 GKN Neural Operator: Graph Kernel Network for Partial Differential Equations https://github.com/zongyi-li/graph-pde ICLR 2020 Workshop https://openreview.net/forum?id=fg2ZFmXFO3
9 MGKN Multipole Graph Neural Operator for Parametric Partial Differential Equations https://github.com/zongyi-li/graph-pde NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/4b21cf96d4cf612f239a6c322b10c8fe-Abstract.html
10 GRAND GRAND: Graph Neural Diffusion https://github.com/twitter-research/graph-neural-pde ICML 2021 https://proceedings.mlr.press/v139/chamberlain21a.html
11 NODEC Neural Ordinary Differential Equation Control of Dynamics on Graphs https://github.com/asikist/nnc https://arxiv.org/abs/2006.09773

List for Over-smoothing

Analyses

Number Paper Code Journal or Conference URL
1 Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning https://github.com/liqimai/gcn AAAI 2018 https://ojs.aaai.org/index.php/AAAI/article/view/11604
2 DeepGCNs: Can GCNs Go as Deep as CNNs? https://github.com/lightaime/deep_gcns ICCV 2019 https://openaccess.thecvf.com/content_ICCV_2019/html/Li_DeepGCNs_Can_GCNs_Go_As_Deep_As_CNNs_ICCV_2019_paper.html
3 Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View AAAI 2020 https://ojs.aaai.org//index.php/AAAI/article/view/5747
4 Graph Neural Networks Exponentially Lose Expressive Power for Node Classification https://github.com/delta2323/gnn-asymptotics ICLR 2020 https://openreview.net/forum?id=S1ldO2EFPr
5 A Note on Over-Smoothing for Graph Neural Networks https://github.com/Chen-Cai-OSU/GNN-Over-Smoothing ICML 2020 Workshop https://arxiv.org/abs/2006.13318
6 Revisiting Over-smoothing in Deep GCNs https://arxiv.org/abs/2003.13663
7 Measuring and Improving the Use of Graph Information in Graph Neural Networks https://github.com/yifan-h/CS-GNN ICLR 2020 https://openreview.net/forum?id=rkeIIkHKvS
8 Simple and Deep Graph Convolutional Networks https://github.com/chennnM/GCNII ICML 2020 https://proceedings.mlr.press/v119/chen20v.html
9 Graph Neural Networks with Adaptive Residual https://github.com/lxiaorui/AirGNN NeurIPS 2021 https://proceedings.neurips.cc/paper/2021/hash/50abc3e730e36b387ca8e02c26dc0a22-Abstract.html
10 Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks https://arxiv.org/abs/2102.06462

Graph Normalization

Number Norm Paper Code Journal or Conference URL
1 PairNorm PairNorm: Tackling Oversmoothing in GNNs https://github.com/LingxiaoShawn/PairNorm ICLR 2020 https://openreview.net/forum?id=rkecl1rtwB
2 NodeNorm Understanding and Resolving Performance Degradation in Graph Convolutional Networks https://github.com/miafei/NodeNorm CIKM 2021 https://dl.acm.org/doi/abs/10.1145/3459637.3482488
3 DGN Towards Deeper Graph Neural Networks with Differentiable Group Normalization https://github.com/Kaixiong-Zhou/DGN NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/33dd6dba1d56e826aac1cbf23cdcca87-Abstract.html
4 GraphNorm GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training https://github.com/lsj2408/GraphNorm ICML 2021 http://proceedings.mlr.press/v139/cai21e.html

Dropout-like or Sampling

Number Method or GNN Paper Code Journal or Conference URL
1 DropEdge DropEdge: Towards Deep Graph Convolutional Networks on Node Classification https://github.com/DropEdge/DropEdge ICLR 2020 https://openreview.net/forum?id=Hkx1qkrKPr
1 DropEdge Tackling Over-Smoothing for General Graph Convolutional Networks https://github.com/DropEdge/DropEdge IEEE Transactions on Pattern Analysis and Machine Intelligence https://arxiv.org/abs/2008.09864
2 FastGCN FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling https://github.com/matenure/FastGCN ICLR 2018 https://openreview.net/forum?id=rytstxWAW
3 VR-GCN Stochastic Training of Graph Convolutional Networks with Variance Reduction https://github.com/thu-ml/stochastic_gcn ICML 2018 https://proceedings.mlr.press/v80/chen18p.html
4 Adaptive Sampling Towards Fast Graph Representation Learning https://github.com/huangwb/AS-GCN NeurIPS 2018 https://proceedings.neurips.cc/paper/2018/hash/01eee509ee2f68dc6014898c309e86bf-Abstract.html
5 Advancing GraphSAGE with A Data-driven Node Sampling https://github.com/oj9040/GraphSAGE_RL ICLR 2019 workshop https://arxiv.org/abs/1904.12935
6 LADIES Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks https://github.com/acbull/LADIES NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/91ba4a4478a66bee9812b0804b6f9d1b-Abstract.html
7 BBGDC Bayesian Graph Neural Networks with Adaptive Connection Sampling https://github.com/armanihm/GDC ICML 2020 https://proceedings.mlr.press/v119/hasanzadeh20a.html
8 GraphSAINT GraphSAINT: Graph Sampling Based Inductive Learning Method https://github.com/GraphSAINT/GraphSAINT ICLR 2020 https://openreview.net/forum?id=BJe8pkHFwS
9 MVS-GNN Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks KDD 2020 https://dl.acm.org/doi/10.1145/3394486.3403192

List for Over-squashing

Number Method or GNN Paper Code Journal or Conference URL
1 On the Bottleneck of Graph Neural Networks and its Practical Implications https://github.com/tech-srl/bottleneck/ ICLR 2021 https://openreview.net/forum?id=i80OPhOCVH2

List for Graph Transformers

Number Graph Transformer Paper Code Journal or Conference URL
1 GTN Graph Transformer Networks https://github.com/seongjunyun/Graph_Transformer_Networks NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/9d63484abb477c97640154d40595a3bb-Abstract.html
2 HGT Heterogeneous Graph Transformer https://github.com/acbull/pyHGT WWW 2020 https://dl.acm.org/doi/10.1145/3366423.3380027
3 GT A Generalization of Transformer Networks to Graphs https://github.com/graphdeeplearning/graphtransformer AAAI 2021 Workshop https://arxiv.org/abs/2012.09699
4 Graphormer Do Transformers Really Perform Badly for Graph Representation? https://github.com/Microsoft/Graphormer NeurIPS 2021 https://openreview.net/forum?id=OeWooOxFwDa
5 GraphiT GraphiT: Encoding Graph Structure in Transformers https://github.com/inria-thoth/GraphiT https://arxiv.org/abs/2106.05667

List for Graph MLP

Number Graph MLP Paper Code Journal or Conference URL
1 Graph-MLP Graph-MLP: Node Classification without Message Passing in Graph https://github.com/yanghu819/Graph-MLP https://arxiv.org/abs/2106.04051

List for Graph Autoencoders (GAE)

Number GAE Paper Code Journal or Conference URL
1 DNGR Deep Neural Networks for Learning Graph Representations AAAI 2016 https://ojs.aaai.org/index.php/AAAI/article/view/10179
2 SDNE Structural Deep Network Embedding
3 DVNE Deep Variational Network Embedding in Wasserstein Space KDD 2016 https://www.kdd.org/kdd2016/subtopic/view/structural-deep-network-embedding
4 VGAE Variational Graph Auto-Encoders https://github.com/tkipf/gae
5 GC-MC Graph Convolutional Matrix Completion https://github.com/riannevdberg/gc-mc https://arxiv.org/abs/1706.02263
6 ARVGA Adversarially regularized graph autoencoder for graph embedding IJCAI 2018 https://dl.acm.org/doi/10.5555/3304889.3305023
7 NetRA Learning deep network representations with adversarially regularized autoencoders KDD 2018 https://dl.acm.org/doi/10.1145/3219819.3220000
8 DeepGMG Learning deep generative models of graphs https://arxiv.org/abs/1803.03324
9 GraphRNN GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models https://github.com/JiaxuanYou/graph-generation ICML 2018 https://proceedings.mlr.press/v80/you18a.html
10 GraphVAE Graphvae: Towards generation of small graphs using variational autoencoders ICANN 2018 https://link.springer.com/chapter/10.1007/978-3-030-01418-6_41
11 Constrained generation of semantically valid graphs via regularizing variational autoencoders NeurISP 2018 https://proceedings.neurips.cc/paper/2018/hash/1458e7509aa5f47ecfb92536e7dd1dc7-Abstract.html
12 Gravity Graph VAE and Gravity Graph AE Gravity-Inspired Graph Autoencoders for Directed Link Prediction https://github.com/deezer/gravity_graph_autoencoders CIKM 2019 https://dl.acm.org/doi/abs/10.1145/3357384.3358023

List for Graph Generative Adversarial Networks (GGAN)

Number GGAN Paper Code Journal or Conference URL
1 GraphGAN GraphGAN: Graph Representation Learning with Generative Adversarial Nets https://github.com/hwwang55/GraphGAN AAAI 2018 https://ojs.aaai.org/index.php/AAAI/article/view/11872
2 MolGAN MolGAN: An implicit generative model for small molecular graphs https://arxiv.org/abs/1805.11973
3 NetGAN NetGAN: Generating graphs via random walks ICML 2018 http://proceedings.mlr.press/v80/bojchevski18a.html

List for graph pre-training

Number Pre-training mathod Paper Code Journal or Conference URL
1 Strategies for Pre-training Graph Neural Networks https://github.com/snap-stanford/pretrain-gnns/ ICLR 2020 https://openreview.net/forum?id=HJlWWJSFDH
2 GCC GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training https://github.com/THUDM/GCC KDD 2020 https://dl.acm.org/doi/10.1145/3394486.3403168

List for GNN explainers

Number GNN or method Paper Code Journal or Conference URL
1 GNNExplainer GNNExplainer: Generating Explanations for Graph Neural Networks https://github.com/RexYing/gnn-model-explainer NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/d80b7040b773199015de6d3b4293c8ff-Abstract.html
2 PGExplainer Parameterized Explainer for Graph Neural Network https://github.com/flyingdoog/PGExplainer NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/e37b08dd3015330dcbb5d6663667b8b8-Abstract.html
3 PGM-Explainer PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks https://github.com/vunhatminh/PGMExplainer NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/8fb134f258b1f7865a6ab2d935a897c9-Abstract.html
4 XGNN XGNN: Towards Model-Level Explanations of Graph Neural Networks KDD 2020 https://www.kdd.org/kdd2020/accepted-papers/view/xgnn-towards-model-level-explanations-of-graph-neural-networks
5 Gem Generative Causal Explanations for Graph Neural Networks https://github.com/wanyu-lin/ICML2021-Gem ICML 2021 https://proceedings.mlr.press/v139/lin21d.html
6 When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods KDD 2021 https://dl.acm.org/doi/abs/10.1145/3447548.3467283

List for Graph Adversarial Attacks and Defenses

Number method Paper Code Journal or Conference URL
1 Adversarial Attacks on Neural Networks for Graph Data https://github.com/danielzuegner/nettack KDD 2018 https://dl.acm.org/doi/10.1145/3219819.3220078
2 Certifiable Robustness and Robust Training for Graph Convolutional Networks https://github.com/danielzuegner/robust-gcn KDD 2020 https://dl.acm.org/doi/abs/10.1145/3394486.3403217
3 Adversarial Attacks on Graph Neural Networks via Meta Learning ICLR 2019 https://openreview.net/forum?id=Bylnx209YX
4 Adversarial Attacks on Node Embeddings via Graph Poisoning https://github.com/abojchevski/node_embedding_attack ICML 2019 https://proceedings.mlr.press/v97/bojchevski19a.html
5 GNNGuard GNNGuard: Defending Graph Neural Networks against Adversarial Attacks https://github.com/mims-harvard/GNNGuard NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/690d83983a63aa1818423fd6edd3bfdb-Abstract.html
6 Detection and Defense of Topological Adversarial Attacks on Graphs ICML 2021 https://proceedings.mlr.press/v130/zhang21i.html
7 GCN-LFR Not All Low-Pass Filters are Robust in Graph Convolutional Networks NeurIPS 2021 https://openreview.net/forum?id=bDdfxLQITtu

List for Others

Number GNN or method Paper Code Journal or Conference URL
1 Contrastive Multi-View Representation Learning on Graphs https://github.com/kavehhassani/mvgrl ICML 2020 https://proceedings.mlr.press/v119/hassani20a.html
2 Benchmarking GNNs Benchmarking Graph Neural Networks https://github.com/graphdeeplearning/benchmarking-gnns https://arxiv.org/abs/2003.00982
3 Interpreting and Unifying Graph Neural Networks with An Optimization Framework WWW 2021 https://dl.acm.org/doi/10.1145/3442381.3449953
4 What graph neural networks cannot learn: depth vs width ICLR 2020 https://openreview.net/forum?id=B1l2bp4YwS
5 Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework https://github.com/BUPT-GAMMA/CPF WWW 2021 https://dl.acm.org/doi/abs/10.1145/3442381.3450068
6 SUGAR SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism https://github.com/RingBDStack/SUGAR WWW 2021 https://dl.acm.org/doi/10.1145/3442381.3449822
7 Towards Sparse Hierarchical Graph Classifiers NeurIPS 2018 Workshop https://arxiv.org/abs/1811.01287
8 OGB Open Graph Benchmark: Datasets for Machine Learning on Graphs https://github.com/snap-stanford/ogb NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html
9 AdaGCN AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models https://github.com/datake/AdaGCN ICLR 2021 https://openreview.net/forum?id=QkRbdiiEjM
10 BGNN Bilinear Graph Neural Network with Neighbor Interactions https://github.com/zhuhm1996/bgnn IJCAI 2020 https://www.ijcai.org/proceedings/2020/202
11 RevGNN-Deep and RevGNN-Wide Training Graph Neural Networks with 1000 Layers https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_proteins ICML 2021 https://proceedings.mlr.press/v139/li21o.html
12 OGB-LSC OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs https://arxiv.org/abs/2103.09430
13 DrGCNs Dimensional Reweighting Graph Convolutional Networks https://arxiv.org/abs/1907.02237
14 GAS GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings https://github.com/rusty1s/pyg_autoscale ICML 2021 http://proceedings.mlr.press/v139/fey21a.html
15 TWIRLS Graph Neural Networks Inspired by Classical Iterative Algorithms https://github.com/FFTYYY/TWIRLS ICML 2021 http://proceedings.mlr.press/v139/yang21g.html
16 GAT-Lip Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks ICML 2021 http://proceedings.mlr.press/v139/dasoulas21a.html
17 Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective https://github.com/balcilar/gnn-spectral-expressive-power ICLR 2021 https://openreview.net/forum?id=-qh0M9XWxnv
18 Deep Graph Neural Networks with Shallow Subgraph Samplers https://github.com/facebookresearch/shaDow_GNN https://arxiv.org/abs/2012.01380
19 Large-scale graph representation learning with very deep GNNs and self-supervision https://github.com/deepmind/deepmind-research/tree/master/ogb_lsc https://arxiv.org/abs/2107.09422
20 GCN-LPA Unifying Graph Convolutional Neural Networks and Label Propagation https://github.com/hwwang55/GCN-LPA Unifying Graph Convolutional Neural Networks and Label Propagation
21 L-GCN and L2-GCN L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks https://github.com/VITA-Group/L2-GCN CVPR 2020 https://openaccess.thecvf.com/content_CVPR_2020/html/You_L2-GCN_Layer-Wise_and_Learned_Efficient_Training_of_Graph_Convolutional_Networks_CVPR_2020_paper.html
22 A Fair Comparison of Graph Neural Networks for Graph Classification https://github.com/diningphil/gnn-comparison ICLR 2020 https://openreview.net/forum?id=HygDF6NFPB
23 GIB Graph Information Bottleneck https://github.com/snap-stanford/GIB NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/ebc2aa04e75e3caabda543a1317160c0-Abstract.html
24 ResRGAT Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies https://github.com/lukovnikov/resrgat ICML 2021 https://proceedings.mlr.press/v139/lukovnikov21a.html
25 Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth ICML 2021 https://proceedings.mlr.press/v139/xu21k.html
26 MinGE Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks https://github.com/RingBDStack/MinGE IJCAI 2021 https://www.ijcai.org/proceedings/2021/381
27 A Flexible Generative Framework for Graph-based Semi-supervised Learning https://github.com/jiaqima/G3NN NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/e0ab531ec312161511493b002f9be2ee-Abstract.html
28 GRAND Graph Random Neural Networks for Semi-Supervised Learning on Graphs https://github.com/THUDM/GRAND NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/fb4c835feb0a65cc39739320d7a51c02-Abstract.html
29 Approximation Ratios of Graph Neural Networks for Combinatorial Problems NeurIPS 2019 https://proceedings.neurips.cc/paper/2019/hash/635440afdfc39fe37995fed127d7df4f-Abstract.html
30 Can Graph Neural Networks Count Substructures? https://github.com/leichen2018/GNN-Substructure-Counting NeurIPS 2020 https://proceedings.neurips.cc/paper/2020/hash/75877cb75154206c4e65e76b88a12712-Abstract.html

List for Surveys

Number Paper Journal or Conference URL
1 Graph Neural Networks: A Review of Methods and Applications AI Open https://www.sciencedirect.com/science/article/pii/S2666651021000012
2 A Comprehensive Survey on Graph Neural Networks IEEE Transactions on Neural Networks and Learning Systems https://ieeexplore.ieee.org/abstract/document/9046288
3 Deep Learning on Graphs: A Survey IEEE Transactions on Knowledge and Data Engineering https://ieeexplore.ieee.org/abstract/document/9039675
4 Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks https://arxiv.org/abs/2107.10234

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A list for GNNs and related works.

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