/awesome-graph-papers

A collection of research papers on graph.

Apache License 2.0Apache-2.0

awesome-graph-papers

A collection of research papers on graph.

GCN

这里整理了一些图卷积文章，参考了 pytorch-geometric 和 dgl 中的图卷积函数。

  • Semi-Supervised Classification with Graph Convolutional Networks GCNConv ICLR 2017[pdf] [code]
  • Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering ChebConv NIPS 2016 [pdf]
  • Inductive Representation Learning on Large Graphs SAGEConv NIPS 2017 [pdf]
  • Inductive Representation Learning on Large Graphs CuGraphSAGEConv NIPS 2017 [pdf]
  • Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks GraphConv AAAI 2019 [pdf]
  • Learning Representations of Irregular Particle-detector Geometry with Distance-weighted Graph Networks GravNetConv EPJ C 2019 [pdf]
  • Gated Graph Sequence Neural Networks GatedGraphConv ICLR 2016 [pdf]
  • Residual Gated Graph ConvNets ResGateGraphConv ICLR 2018 [pdf]
  • Graph Attention Networks GATConv ICLR 2018 [pdf]
  • Graph Attention Networks CuGraphGATConv ICLR 2018 [pdf]
  • Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective FusedGATConv MLSys 2022 [pdf]
  • How Powerful are Graph Neural Networks? GATv2Conv ICLR 2019 [pdf]
  • Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification TransformerConv IJCAI 2021 [pdf]
  • Attention-based Graph Neural Network for Semi-Supervised Learning AGNNConv ICLR 2018 [pdf]
  • Topology Adaptive Graph Convolutional Networks TAGConv arxiv 2017 [pdf]
  • How Powerful are Graph Neural Networks? GINConv ICLR 2019 [pdf]
  • Strategies for Pre-training Graph Neural Networks GINEConv ICLR 2020 [pdf]
  • Graph Neural Networks with Convolutional ARMA Filters ARMAConv IEEE Trans 2022 [pdf]
  • Simplifying Graph Convolutional Networks SGConv ICML 2019 [pdf]
  • Simple Spectral Graph Convolution SSGConv ICLR 2021 [pdf]
  • Predict then Propagate: Graph Neural Networks meet Personalized PageRank APPNPConv ICLR 2019 [pdf]
  • Convolutional Networks on Graphs for Learning Molecular Fingerprints MFConv NIPS 2015 [pdf]
  • Modeling Relational Data with Graph Convolutional Networks RGCNConv ESWC 2018 [pdf]
  • Modeling Relational Data with Graph Convolutional Networks FastRGCNConv ESWC 2018 [pdf]
  • Modeling Relational Data with Graph Convolutional Networks CuGraphRGCNConv ESWC 2018 [pdf]
  • Relational Graph Attention Networks RGAConv arxiv 2019 [pdf]
  • Signed Graph Convolutional Network SignedConv ICDM 2018 [pdf]
  • Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks DNAConv arxiv 2019 [pdf]
  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation PointConv CVPR 2017 [pdf]
  • Dynamic Graph CNN for Learning on Point Clouds EdgeConv ACM Trans 2019 [pdf]
  • PointCNN: Convolution On X-Transformed Points XConv NIPS 2018 [pdf]
  • PPFNet: Global Context Aware Local Features for Robust 3D Point Matching PPFConv CVPR 2018 [pdf]
  • FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis FeaStConv CVPR 2018 [pdf]
  • Point Transformer PointTransformerConv ICCV 2021 [pdf]
  • Hypergraph Convolution and Hypergraph Attention HyperGraphConv Pattern Recognit 2021 [pdf]
  • ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations LEConv AAAI 2020 [pdf]
  • Principal Neighbourhood Aggregation for Graph Nets PNAConv NeurIPS 2020 [pdf]
  • Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks ClusterConv KDD 2019 [pdf]
  • DeeperGCN: All You Need to Train Deeper GCNs GENConv arxiv 2020 [pdf]
  • Simple and Deep Graph Convolutional Networks GCN2Conv ICML 2020 [pdf]
  • Path Integral Based Convolution and Pooling for Graph Neural Networks PANConv NIPS 2020 [pdf]
  • A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction WLConv Nauchno-Technicheskaya Informatsiya 1968 [pdf]
  • GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation FiLMConv ICML 2020 [pdf]
  • How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision SurperGATConv ICLR 2021 [pdf]
  • Beyond Low-Frequency Information in Graph Convolutional Networks FAConv AAAI 2021 [pdf]
  • Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions EGConv arXiv 2021 [pdf]
  • Pathfinder Discovery Networks for Neural Message Passing PDNConv WWW 2021 [pdf]
  • Design Space for Graph Neural Networks GeneralConv NIPS 2020 [pdf]
  • Heterogeneous Graph Transformer HGTConv WWW 2020 [pdf]
  • Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction HEATConv arxiv 2021 [pdf]
  • Heterogenous Graph Attention Network HANConv WWW 2019 [pdf]
  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation LGConv SIGIR 2020 [pdf]
  • Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud PointGNNConv CVPR 2020 [pdf]
  • Recipe for a General, Powerful, Scalable Graph Transformer GPSConv NIPS 2022 [pdf]
  • Anti-Symmetric DGN: a stable architecture for Deep Graph Networks AntiSymmericConv ICLR 2023 [pdf]
  • Edge Directionality Improves Learning on Heterophilic Graphs DirGNNConv LoG 2023 [pdf]
  • MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing MixHopConv ICML 2019 [pdf]
  • Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs GMMConv CVPR 2017 [pdf]
  • Neural Message Passing for Quantum Chemistry NNConv ICML 2017 [pdf]

Graph Pooling

常用的简单的图池化方法有 SumPooling, AvgPooling 和 MaxPooling，这里列举一些其他的更加复杂的图池化方法，参考了 pytorch-geometric 和 dgl 中的图池化函数。

  • An End-to-End Deep Learning Architecture for Graph Classification SortPooling AAAI 2018 [pdf]
  • Graph U-Nets TopKPooling ICML 2019 [pdf]
  • Self-Attention Graph Pooling SAGPooling ICML 2019 [pdf]
  • Gated Graph Sequence Neural Networks GlobalAttentionPooling ICLR 2016 [pdf]
  • Order Matters: Sequence to sequence for sets Set2SetPooling ICLR 2016 [pdf]
  • Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks SetTransformerPooling ICML 2019 [pdf]
  • Towards Graph Pooling by Edge Contraction and Edge Contraction Pooling for Graph Neural Networks EdgePooling ICML 2019 arxiv 2019 [pdf][pdf]
  • ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations ASAPooling AAAI 2020 [pdf]
  • Path Integral Based Convolution and Pooling for Graph Neural Networks PANPooling NIPS 2020 [pdf]
  • Memory-Based Graph Networks MemPooling ICLR 2020 [pdf]