/awesome-deep-gnn

Papers about developing deep Graph Neural Networks (GNNs)

awesome-deep-gnn

Papers about developing deep Graph Neural Networks (GNNs). Investigations about over-smoothing and over-squashing problem in GNNs are also included here.

Please feel free to submit a pull request if you want to add good papers.

2021

  • [arXiv 2021] Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks [Paper]
  • [arXiv 2021] Graph Neural Networks Inspired by Classical Iterative Algorithms [Paper]

  • [ICML 2021] Training Graph Neural Networks with 1000 Layers [Paper][Code]
  • [ICML 2021] Directional Graph Networks [Paper][Code]
  • [ICLR 2021] On the Bottleneck of Graph Neural Networks and its Practical Implications [Paper][Code]
  • [ICLR 2021] Adaptive Universal Generalized PageRank Graph Neural Network [Paper][Code]
  • [ICLR 2021] Simple Spectral Graph Convolution [Paper]

2020

  • [arXiv 2020] Deep Graph Neural Networks with Shallow Subgraph Samplers [Paper]
  • [arXiv 2020] Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective [Paper]
  • [arXiv 2020] Tackling Over-Smoothing for General Graph Convolutional Networks [Paper]
  • [arXiv 2020] DeeperGCN: All You Need to Train Deeper GCNs [Paper][Code]
  • [arXiv 2020] Effective Training Strategies for Deep Graph Neural Networks [Paper][Code]
  • [arXiv 2020] Revisiting Over-smoothing in Deep GCNs [paper]

  • [NeurIPS 2020] Graph Random Neural Networks for Semi-Supervised Learning on Graphs [Paper][Code]
  • [NeurIPS 2020] Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks [Paper][Code]
  • [NeurIPS 2020] Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks [Paper][Code]
  • [NeurIPS 2020] Towards Deeper Graph Neural Networks with Differentiable Group Normalization [Paper]
  • [ICML 2020 Workshop GRL+] A Note on Over-Smoothing for Graph Neural Networks [Paper]
  • [ICML 2020] Bayesian Graph Neural Networks with Adaptive Connection Sampling [Paper]
  • [ICML 2020] Continuous Graph Neural Networks [Paper]
  • [ICML 2020] Simple and Deep Graph Convolutional Networks [Paper][Code]
  • [KDD 2020] Towards Deeper Graph Neural Networks [Paper][Code]
  • [ICLR 2020] Graph Neural Networks Exponentially Lose Expressive Power for Node Classification [Paper][Code]
  • [ICLR 2020] DropEdge: Towards Deep Graph Convolutional Networks on Node Classification [Paper][Code]
  • [ICLR 2020] PairNorm: Tackling Oversmoothing in GNNs [Paper][Code]
  • [ICLR 2020] Measuring and Improving the Use of Graph Information in Graph Neural Networks [Paper][Code]
  • [AAAI 2020] Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View [Paper]

Before 2020

  • [arXiv 2019] Revisiting Graph Neural Networks: All We Have is Low-Pass Filters [Paper]

  • [NeurIPS 2019] Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks [Paper]
  • [ICLR 2019] Predict then Propagate: Graph Neural Networks meet Personalized PageRank [Paper][Code]
  • [ICCV 2019] DeepGCNs: Can GCNs Go as Deep as CNNs? [Paper][Code(Pytorch)][Code(TensorFlow)]
  • [ICML 2018] Representation Learning on Graphs with Jumping Knowledge Networks [Paper]
  • [AAAI 2018] Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning [Paper]