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	ChebNet	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	NeurIPS 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	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	Snowball and Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	https://github.com/PwnerHarry/Stronger_GCN	NeurIPS 2019	https://proceedings.neurips.cc/paper/2019/hash/ccdf3864e2fa9089f9eca4fc7a48ea0a-Abstract.html
16	GBP	Scalable Graph Neural Networks via Bidirectional Propagation	https://github.com/chennnM/GBP	NeurIPS 2020	https://proceedings.neurips.cc/paper/2020/hash/a7789ef88d599b8df86bbee632b2994d-Abstract.html
17	FisherGCN	Fisher-Bures Adversary Graph Convolutional Networks	https://github.com/D61-IA/FisherGCN	ICML 2020	http://proceedings.mlr.press/v115/sun20a.html
18	DGCN	Directed Graph Convolutional Network			https://arxiv.org/abs/2004.13970
19	SIGN	SIGN: Scalable Inception Graph Neural Networks		ICML 2020 Workshop	https://arxiv.org/abs/2004.11198
20	DiGCN	Digraph Inception Convolutional Networks	https://github.com/flyingtango/DiGCN	NeurIPS 2020	https://proceedings.neurips.cc/paper/2020/hash/cffb6e2288a630c2a787a64ccc67097c-Abstract.html
21	HKGCN	Generalizing Graph Convolutional Networks via Heat Kernel			https://openreview.net/forum?id=yBJihVXahXc
22	S2GC	Simple Spectral Graph Convolution	https://github.com/allenhaozhu/SSGC	ICLR 2021	https://openreview.net/forum?id=CYO5T-YjWZV
23	GPR-GNN	Adaptive Universal Generalized PageRank Graph Neural Network	https://github.com/jianhao2016/GPRGNN	ICLR 2021	https://openreview.net/forum?id=n6jl7fLxrP
24	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
25	MagNet	MagNet: A Neural Network for Directed Graphs	https://github.com/matthew-hirn/magnet	NeurIPS 2021	https://openreview.net/forum?id=TRDAFiwDq8A
26	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
27	EdgeNet	EdgeNets: Edge Varying Graph Neural Networks		IEEE Transactions on Pattern Analysis and Machine Intelligence	https://ieeexplore.ieee.org/abstract/document/9536420
28	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
29	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
30	AirGNN	Graph Neural Networks with Adaptive Residual	https://github.com/lxiaorui/AirGNN	NeurIPS 2021	https://proceedings.neurips.cc/paper/2021/hash/50abc3e730e36b387ca8e02c26dc0a22-Abstract.html
31	ADC	Adaptive Diffusion in Graph Neural Networks	https://github.com/abcbdf/ADC	NeurIPS 2021	https://proceedings.neurips.cc/paper/2021/hash/c42af2fa7356818e0389593714f59b52-Abstract.html
32	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
33	BM-GCN	Block Modeling-Guided Graph Convolutional Neural Networks	https://github.com/hedongxiao-tju/BM-GCN	AAAI 2022	https://ojs.aaai.org/index.php/AAAI/article/view/20319
34	Ortho-GConv	Orthogonal Graph Neural Networks	https://github.com/KaiGuo20/Ortho-GConv	AAAI 2022	https://ojs.aaai.org/index.php/AAAI/article/view/20316
35	pGNN	p-Laplacian Based Graph Neural Networks	https://github.com/guoji-fu/pgnns	ICML 2022	https://proceedings.mlr.press/v162/fu22e.html
36	Spec-GN and Norm-GN	A New Perspective on the Effects of Spectrum in Graph Neural Networks	https://github.com/qslim/gnn-spectrum	ICML 2022	https://proceedings.mlr.press/v162/yang22n.html
37	JacobiConv	How Powerful are Spectral Graph Neural Networks	https://github.com/GraphPKU/JacobiConv	ICML 2022	https://proceedings.mlr.press/v162/wang22am.html
38	G2CN	G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters		ICML 2022	https://proceedings.mlr.press/v162/li22h.html
39	ChebNetII	Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited		NeurIPS 2022	https://arxiv.org/abs/2202.03580
40	EvenNet	EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks		NeurIPS 2022	https://arxiv.org/abs/2205.13892
41	SigMagNet	SigMaNet: One Laplacian to Rule Them All			https://arxiv.org/abs/2205.13459
42	Spectral-SGCN-I, Spectral-SGCN-II, Spectral-S2GCN, and Singned-Magnet	Signed Graph Neural Networks: A Frequency Perspective			https://arxiv.org/abs/2208.07323

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

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	https://github.com/shinkyuy/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	GINE	Graph convolutions that can finally model local structure	https://github.com/RBrossard/GINEPLUS		https://arxiv.org/abs/2011.15069
12	DGN	Directional Graph Networks	https://github.com/Saro00/DGN	ICML 2021	http://proceedings.mlr.press/v139/beani21a.html
13	Elastic GNN	Elastic Graph Neural Networks	https://github.com/lxiaorui/ElasticGNN	ICML 2021	https://proceedings.mlr.press/v139/liu21k.html
14	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
15		Don't stack layers in graph neural networks, wire them randomly		ICLR 2021 Workshop	https://openreview.net/forum?id=xFH_wIFy1Je
16	CIN	Weisfeiler and Lehman Go Cellular: CW Networks	https://github.com/twitter-research/cwn	NeurIPS 2021	https://openreview.net/forum?id=uVPZCMVtsSG
17	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
18	NDLS	Node Dependent Local Smoothing for Scalable Graph Learning	https://github.com/zwt233/ndls	NeurIPS 2021	https://openreview.net/forum?id=ekKaTdleJVq
19	GraphSNN	A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"		ICLR 2022	https://openreview.net/forum?id=uxgg9o7bI_3
20	DS-GNN	Equivariant Subgraph Aggregation Networks		ICLR 2022	https://openreview.net/forum?id=dFbKQaRk15w
21	PEG	Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks	https://github.com/graph-com/peg	ICLR 2022	https://openreview.net/forum?id=e95i1IHcWj

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	SuperGAT	How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision	https://github.com/dongkwan-kim/SuperGAT	ICLR 2021	https://openreview.net/forum?id=Wi5KUNlqWty
14	GATv2	How Attentive are Graph Attention Networks?	https://github.com/tech-srl/how_attentive_are_gats	ICLR 2022	https://openreview.net/forum?id=F72ximsx7C1

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	GEN	DeeperGCN: All You Need to Train Deeper GCNs	https://github.com/lightaime/deep_gcns_torch		https://arxiv.org/abs/2006.07739
3	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
4	PNA	Principal Neighbourhood Aggregation for Graph Nets	https://github.com/lukecavabarrett/pna	NeurIPS 2020	https://proceedings.neurips.cc/paper/2020/hash/99cad265a1768cc2dd013f0e740300ae-Abstract.html
5	GNNML	Breaking the Limits of Message Passing Graph Neural Networks	https://github.com/balcilar/gnn-matlang	ICML 2021	https://proceedings.mlr.press/v139/balcilar21a.html
6	EGC	Do We Need Anistropic Graph Neural Networks?	https://github.com/shyam196/egc	ICLR 2022	https://openreview.net/forum?id=hl9ePdHO4_s

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		Learning continuous-time PDEs from sparse data with graph neural networks		ICLR 2021	https://openreview.net/forum?id=aUX5Plaq7Oy
11	GRAND	GRAND: Graph Neural Diffusion	https://github.com/twitter-research/graph-neural-pde	ICML 2021	https://proceedings.mlr.press/v139/chamberlain21a.html
12	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
11	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs			https://arxiv.org/abs/2202.04579

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
2		Understanding over-squashing and bottlenecks on graphs via curvature		ICLR 2022	https://openreview.net/forum?id=7UmjRGzp-A

List for Graph Transformers

Number	Graph Transformer	Paper	Code	Journal or Conference	URL
1	Graph-Bert	Graph-Bert: Only Attention is Needed for Learning Graph Representations	https://github.com/jwzhanggy/Graph-Bert		https://arxiv.org/abs/2001.05140
2	GTN	Graph Transformer Networks	https://github.com/seongjunyun/Graph_Transformer_Networks	NeurIPS 2019	https://proceedings.neurips.cc/paper/2019/hash/9d63484abb477c97640154d40595a3bb-Abstract.html
3	HGT	Heterogeneous Graph Transformer	https://github.com/acbull/pyHGT	WWW 2020	https://dl.acm.org/doi/10.1145/3366423.3380027
4	GT	A Generalization of Transformer Networks to Graphs	https://github.com/graphdeeplearning/graphtransformer	AAAI 2021 Workshop	https://arxiv.org/abs/2012.09699
5	SAN	Rethinking Graph Transformers with Spectral Attention	https://github.com/DevinKreuzer/SAN	NeurIPS 2021	https://openreview.net/forum?id=huAdB-Tj4yG
6	Graphormer	Do Transformers Really Perform Badly for Graph Representation?	https://github.com/Microsoft/Graphormer	NeurIPS 2021	https://openreview.net/forum?id=OeWooOxFwDa
7	Graphormer	Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets	https://github.com/Microsoft/Graphormer		https://arxiv.org/abs/2203.04810
8	Gophormer	Gophormer: Ego-Graph Transformer for Node Classification			https://arxiv.org/abs/2110.13094
9	SEA	SEA: Graph Shell Attention in Graph Neural Networks			https://arxiv.org/pdf/2110.10674.pdf
10	GraphiT	GraphiT: Encoding Graph Structure in Transformers	https://github.com/inria-thoth/GraphiT		https://arxiv.org/abs/2106.05667
11	Coarformer	Coarformer: Transformer for large graph via graph coarsening			https://openreview.net/forum?id=fkjO_FKVzw
12	GKAT	From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers	https://github.com/hl-hanlin/gkat		https://arxiv.org/abs/2107.07999
13	FeTA	Investigating Expressiveness of Transformer in Spectral Domain for Graphs			https://arxiv.org/abs/2201.09332
14	GRPE	GRPE: Relative Positional Encoding for Graph Transformer	https://github.com/lenscloth/grpe		https://arxiv.org/abs/2201.12787

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
2	N2N	Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization	https://github.com/dongwei156/n2n		https://arxiv.org/abs/2203.12265

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
7	MotifExplainer	MotifExplainer: a Motif-based Graph Neural Network Explainer			https://arxiv.org/abs/2202.00519

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	FLAG	Robust Optimization as Data Augmentation for Large-scale Graphs	https://github.com/devnkong/FLAG		https://arxiv.org/abs/2010.09891
4		Interpreting and Unifying Graph Neural Networks with An Optimization Framework		WWW 2021	https://dl.acm.org/doi/10.1145/3442381.3449953
5		What graph neural networks cannot learn: depth vs width		ICLR 2020	https://openreview.net/forum?id=B1l2bp4YwS
6		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
7	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
8		Towards Sparse Hierarchical Graph Classifiers		NeurIPS 2018 Workshop	https://arxiv.org/abs/1811.01287
9	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
10	AdaGCN	AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models	https://github.com/datake/AdaGCN	ICLR 2021	https://openreview.net/forum?id=QkRbdiiEjM
11	BGNN	Bilinear Graph Neural Network with Neighbor Interactions	https://github.com/zhuhm1996/bgnn	IJCAI 2020	https://www.ijcai.org/proceedings/2020/202
12	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
13	OGB-LSC	OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs			https://arxiv.org/abs/2103.09430
14	DrGCNs	Dimensional Reweighting Graph Convolutional Networks			https://arxiv.org/abs/1907.02237
15	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
16	TWIRLS	Graph Neural Networks Inspired by Classical Iterative Algorithms	https://github.com/FFTYYY/TWIRLS	ICML 2021	http://proceedings.mlr.press/v139/yang21g.html
17	GAT-Lip	Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks		ICML 2021	http://proceedings.mlr.press/v139/dasoulas21a.html
18		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
19		Deep Graph Neural Networks with Shallow Subgraph Samplers	https://github.com/facebookresearch/shaDow_GNN		https://arxiv.org/abs/2012.01380
20		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
21	GCN-LPA	Unifying Graph Convolutional Neural Networks and Label Propagation	https://github.com/hwwang55/GCN-LPA		Unifying Graph Convolutional Neural Networks and Label Propagation
22	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
23		A Fair Comparison of Graph Neural Networks for Graph Classification	https://github.com/diningphil/gnn-comparison	ICLR 2020	https://openreview.net/forum?id=HygDF6NFPB
24	CurvGN	Curvature Graph Network		ICLR 2020	https://openreview.net/forum?id=BylEqnVFDB
25	GIB	Graph Information Bottleneck	https://github.com/snap-stanford/GIB	NeurIPS 2020	https://proceedings.neurips.cc/paper/2020/hash/ebc2aa04e75e3caabda543a1317160c0-Abstract.html
26	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
27		Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth		ICML 2021	https://proceedings.mlr.press/v139/xu21k.html
28	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
29		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
30	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
31		Approximation Ratios of Graph Neural Networks for Combinatorial Problems		NeurIPS 2019	https://proceedings.neurips.cc/paper/2019/hash/635440afdfc39fe37995fed127d7df4f-Abstract.html
32		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
33	GNN-FiLM	GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation	https://github.com/microsoft/tf-gnn-samples	ICML 2020	https://proceedings.mlr.press/v119/brockschmidt20a.html
34		Graph Attention Retrospective			https://arxiv.org/abs/2202.13060
35		GraphWorld: Fake Graphs Bring Real Insights for GNNs			https://arxiv.org/abs/2203.00112
36	GRAND+	GRAND+: Scalable Graph Random Neural Networks	https://github.com/THUDM/GRAND-plus		https://arxiv.org/abs/2203.06389
37	SAGN	Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training	https://github.com/skepsun/SAGN_with_SLE		https://arxiv.org/abs/2104.09376
38	GAMLP	Graph Attention Multi-Layer Perceptron	https://github.com/pku-dair/gamlp
39		Expressiveness and Approximation Properties of Graph Neural Networks		ICLR 2022	https://openreview.net/forum?id=wIzUeM3TAU

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	Explainability in Graph Neural Networks: A Taxonomic Survey		https://arxiv.org/abs/2012.15445
5	Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks		https://arxiv.org/abs/2107.10234