part1/經典款論文
1. KDD 2016，Node2vec 經典必讀第一篇，平衡同質性和結構性
《node2vec: Scalable Feature Learning for Networks》
2. WWW2015，LINE 1階+2階相似度
《Line: Large-scale information network embedding》
3. KDD 2016，SDNE 多層自編碼器
《Structural deep network embedding》
4. KDD 2017，metapath2vec 異構圖網絡
《metapath2vec: Scalable representation learning for heterogeneous networks》
5. NIPS 2013，TransE 知識圖譜奠基
《Translating Embeddings for Modeling Multi-relational Data》
6. ICLR 2018，GAT attention機制
《Graph Attention Network》
7. NIPS 2017，GraphSAGE 歸納式學習框架
《Inductive Representation Learning on Large Graphs 》
8. ICLR 2017，GCN 圖神經開山之作
《SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS》
9. ICLR 2016，GGNN 門控圖神經網絡
《Gated Graph Sequence Neural Networks》
10. ICML 2017，MPNN 空域卷積消息傳遞框架
《Neural Message Passing for Quantum Chemistry》
part2/熱門款論文
2020年之前
11.[arXiv 2019]Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
重溫圖神經網絡：我們只有低通濾波器
[論文]
https://arxiv.org/abs/1905.09550
12.[NeurIPS 2019]Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
打破天花板：更強的多尺度深度圖卷積網絡
[論文]
https://arxiv.org/abs/1906.02174
13.[ICLR 2019] Predict then Propagate: Graph Neural Networks meet Personalized PageRank
先預測后傳播：圖神經網絡滿足個性化 PageRank
[論文]
https://arxiv.org/abs/1810.05997
[代碼]
https://github.com/klicperajo/ppnp
14.[ICCV 2019]DeepGCNs: Can GCNs Go as Deep as CNNs?
DeepGCN：GCN能像CNN一樣深入嗎？
[論文]
https://arxiv.org/abs/1904.03751
[代碼(Pytorch)]
https://github.com/lightaime/deep_gcns_torch
[代碼(TensorFlow)]
https://github.com/lightaime/deep_gcns
15.[ICML 2018]
Representation Learning on Graphs with Jumping Knowledge Networks
基於跳躍知識網絡的圖表征學習
[論文]
https://arxiv.org/abs/1806.03536
16.[AAAI 2018]Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
深入了解用於半監督學習的圖卷積網絡
[論文]
https://arxiv.org/abs/1801.07606
2020年
17.[arXiv 2020]Deep Graph Neural Networks with Shallow Subgraph Samplers
具有淺子圖采樣器的深圖神經網絡
[論文]
https://arxiv.org/abs/2012.01380
18.[arXiv 2020]Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
從優化的角度重新審視半監督節點分類的圖卷積網絡
[論文]
https://arxiv.org/abs/2009.11469
19.[arXiv 2020]
Tackling Over-Smoothing for General Graph Convolutional Networks
解決通用圖卷積網絡的過度平滑
[論文]
https://arxiv.org/abs/2008.09864
20.[arXiv 2020]DeeperGCN: All You Need to Train Deeper GCNs
DeeperGCN：訓練更深的 GCN 所需的一切
[論文]
https://arxiv.org/abs/2006.07739
[代碼]
https://github.com/lightaime/deep_gcns_torch
21.[arXiv 2020]Effective Training Strategies for Deep Graph Neural Networks
深度圖神經網絡的有效訓練策略
[論文]
https://arxiv.org/abs/2006.07107
[代碼]
https://github.com/miafei/NodeNorm
22.[arXiv 2020]Revisiting Over-smoothing in Deep GCNs
重新審視深度GCN中的過度平滑
[論文]
https://arxiv.org/abs/2003.13663
23.[NeurIPS 2020]Graph Random Neural Networks for Semi-Supervised Learning on Graphs
用於圖上半監督學習的圖隨機神經網絡
[論文]
https://proceedings.neurips.cc/paper/2020/hash/fb4c835feb0a65cc39739320d7a51c02-Abstract.html
[代碼]
https://github.com/THUDM/GRAND
24.[NeurIPS 2020]Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
散射GCN：克服圖卷積網絡中的過度平滑
[論文]
https://proceedings.neurips.cc/paper/2020/hash/a6b964c0bb675116a15ef1325b01ff45-Abstract.html
[代碼]
https://github.com/dms-net/scatteringGCN
25.[NeurIPS 2020]Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Transduction through Gradient Boosting 的優化和泛化分析及其在多尺度圖神經網絡中的應用
[論文]
https://proceedings.neurips.cc/paper/2020/hash/dab49080d80c724aad5ebf158d63df41-Abstract.html
[代碼]
https://github.com/delta2323/GB-GNN
26.[NeurIPS 2020]Towards Deeper Graph Neural Networks with Differentiable Group Normalization
邁向具有可微組歸一化的更深圖神經網絡
[論文]
https://arxiv.org/abs/2006.06972
27.[ICML 2020 Workshop GRL+]A Note on Over-Smoothing for Graph Neural Networks
關於圖神經網絡過度平滑的說明
[論文]
https://arxiv.org/abs/2006.13318
28.[ICML 2020]Bayesian Graph Neural Networks with Adaptive Connection Sampling
具有自適應連接采樣的貝葉斯圖神經網絡
[論文]
https://arxiv.org/abs/2006.04064
29.[ICML 2020]Continuous Graph Neural Networks連續圖神經網絡
[論文]
https://arxiv.org/abs/1912.00967
30.[ICML 2020]Simple and Deep Graph Convolutional Networks簡單和深度圖卷積網絡
[論文]
https://arxiv.org/abs/2007.02133
[代碼]
https://github.com/chennnM/GCNII
31.[KDD 2020] Towards Deeper Graph Neural Networks走向更深的圖神經網絡
[論文]
https://arxiv.org/abs/2007.09296
[代碼]
https://github.com/mengliu1998/DeeperGNN
32.[ICLR 2020]Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
圖神經網絡對節點分類的表達能力呈指數級 下降
[論文]
https://arxiv.org/abs/1905.10947
[代碼]
https://github.com/delta2323/gnn-asymptotics
33.[ICLR 2020] DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
DropEdge：邁向節點分類的深度圖卷積網絡
[Paper]
https://openreview.net/forum?id=Hkx1qkrKPr
[Code]
https://github.com/DropEdge/DropEdge
34.[ICLR 2020] PairNorm: Tackling Oversmoothing in GNNs
PairNorm：解決GNN中的過度平滑問題
[論文]
https://openreview.net/forum?id=rkecl1rtwB
[代碼]
https://github.com/LingxiaoShawn/PairNorm
35.[ICLR 2020]Measuring and Improving the Use of Graph Information in Graph Neural Networks
測量和改進圖神經網絡中圖信息的使用
[論文]
https://openreview.net/forum?id=rkeIIkHKvS
[代碼]
https://github.com/yifan-h/CS-GNN
36.[AAAI 2020]Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
從拓撲角度測量和緩解圖神經網絡的過度平滑問題
[論文]
https://arxiv.org/abs/1909.03211
同學們是不是發現有些論文有代碼，有些論文沒有代碼？學姐建議學概念讀沒代碼的，然后再讀有代碼的，原因的話上周的文章有寫，花幾分鍾看一下【學姐帶你玩AI】公眾號的——《圖像識別深度學習研究方向沒有導師帶該怎么學習》
part3/最新款論文
37.[arXiv 2021]Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
同一枚硬幣的兩面：圖卷積神經網絡中的異質性和過度平滑
[論文]
https://arxiv.org/abs/2102.06462v2
38.[arXiv 2021]Graph Neural Networks Inspired by Classical Iterative Algorithms
受經典迭代算法啟發的圖神經網絡
[論文]
https://arxiv.org/abs/2103.06064
39.[ICML 2021]Training Graph Neural Networks with 1000 Layers
訓練 1000 層圖神經網絡
[論文]
https://arxiv.org/abs/2106.07476
[代碼]
https://github.com/lightaime/deep_gcns_torch
40.[ICML 2021] Directional Graph Networks 方向圖網絡
[論文]
https://arxiv.org/abs/2010.02863
[代碼]
https://github.com/Saro00/DGN
41.[ICLR 2021]On the Bottleneck of Graph Neural Networks and its Practical Implications
關於圖神經網絡的瓶頸及其實際意義
[論文]
https://openreview.net/forum?id=i80OPhOCVH2
[代碼] https://github.com/tech-srl/bottleneck/
42.[ICLR 2021] Adaptive Universal Generalized PageRank Graph Neural Network
[論文]
https://openreview.net/forum?id=n6jl7fLxrP
[代碼]
https://github.com/jianhao2016/GPRGNN
43.[ICLR 2021]Simple Spectral Graph Convolution
簡單的譜圖卷積
[論文]
https://openreview.net/forum?id=CYO5T-YjWZV
地址：https://github.com/mengliu1998/awesome-deep-gnn