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Official DGL Examples and Modules

The folder contains example implementations of selected research papers related to Graph Neural Networks. Note that the examples may not work with incompatible DGL versions.

Overview

Paper node classification link prediction / classification graph property prediction sampling OGB
Network Embedding with Completely-imbalanced Labels ✔️
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks ✔️
Contrastive Multi-View Representation Learning on Graphs ✔️ ✔️
Deep Graph Contrastive Representation Learning ✔️
Graph Random Neural Network for Semi-Supervised Learning on Graphs ✔️
Heterogeneous Graph Transformer ✔️ ✔️
Graph Convolutional Networks for Graphs with Multi-Dimensionally Weighted Edges ✔️ ✔️
SIGN: Scalable Inception Graph Neural Networks ✔️ ✔️
Strategies for Pre-training Graph Neural Networks ✔️
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization ✔️
Graph Neural Networks with convolutional ARMA filters ✔️
Predict then Propagate: Graph Neural Networks meet Personalized PageRank ✔️
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks ✔️ ✔️ ✔️
Deep Graph Infomax ✔️
Hierarchical Graph Representation Learning with Differentiable Pooling ✔️
Representation Learning for Attributed Multiplex Heterogeneous Network ✔️
How Powerful are Graph Neural Networks? ✔️ ✔️ ✔️
Heterogeneous Graph Attention Network ✔️
Simplifying Graph Convolutional Networks ✔️
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective ✔️
Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism ✔️
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing ✔️
Graph Attention Networks ✔️ ✔️
Attention-based Graph Neural Network for Semi-supervised Learning ✔️ ✔️
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Semi-Supervised Classification with Graph Convolutional Networks ✔️ ✔️ ✔️ ✔️
Graph Convolutional Matrix Completion ✔️
Inductive Representation Learning on Large Graphs ✔️ ✔️ ✔️ ✔️
metapath2vec: Scalable Representation Learning for Heterogeneous Networks ✔️
Topology Adaptive Graph Convolutional Networks ✔️
Modeling Relational Data with Graph Convolutional Networks ✔️ ✔️ ✔️
Neural Message Passing for Quantum Chemistry ✔️
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions ✔️
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering ✔️ ✔️
Geometric deep learning on graphs and manifolds using mixture model CNNs ✔️ ✔️
Molecular Graph Convolutions: Moving Beyond Fingerprints ✔️
LINE: Large-scale Information Network Embedding ✔️ ✔️
DeepWalk: Online Learning of Social Representations ✔️ ✔️
Self-Attention Graph Pooling ✔️
Convolutional Networks on Graphs for Learning Molecular Fingerprints ✔️
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation ✔️
Hierarchical Graph Pooling with Structure Learning ✔️
Graph Representation Learning via Hard and Channel-Wise Attention Networks ✔️
Neural Graph Collaborative Filtering ✔️
Graph Cross Networks with Vertex Infomax Pooling ✔️
Towards Deeper Graph Neural Networks ✔️
The PageRank Citation Ranking: Bringing Order to the Web
Fast Suboptimal Algorithms for the Computation of Graph Edit Distance
Speeding Up Graph Edit Distance Computation with a Bipartite Heuristic
A Three-Way Model for Collective Learning on Multi-Relational Data
Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching
Translating Embeddings for Modeling Multi-relational Data
A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Order Matters: Sequence to sequence for sets ✔️
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Complex Embeddings for Simple Link Prediction
Gated Graph Sequence Neural Networks
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Attention Is All You Need
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Dynamic Routing Between Capsules
An End-to-End Deep Learning Architecture for Graph Classification ✔️
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Recurrent Relational Networks
Junction Tree Variational Autoencoder for Molecular Graph Generation
Learning Deep Generative Models of Graphs
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
A graph-convolutional neural network model for the prediction of chemical reactivity
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks ✔️
Graphical Contrastive Losses for Scene Graph Parsing
Dynamic Graph CNN for Learning on Point Clouds
Supervised Community Detection with Line Graph Neural Networks
Text Generation from Knowledge Graphs with Graph Transformers
Temporal Graph Networks For Deep Learning on Dynamic Graphs ✔️
Directional Message Passing for Molecular Graphs ✔️
Link Prediction Based on Graph Neural Networks ✔️ ✔️ ✔️
Variational Graph Auto-Encoders ✔️
Composition-based Multi-Relational Graph Convolutional Networks ✔️
GNNExplainer: Generating Explanations for Graph Neural Networks ✔️
Interaction Networks for Learning about Objects, Relations and Physics ✔️
Representation Learning on Graphs with Jumping Knowledge Networks ✔️
DeeperGCN: All You Need to Train Deeper GCNs ✔️ ✔️
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forcasting ✔️
GaAN: Gated Attention Networks for Learning on large and Spatiotemporal Graphs ✔️

2021

  • Ivanov et al. Boost then Convolve: Gradient Boosting Meets Graph Neural Networks. Paper link.
    • Example code: PyTorch
    • Tags: semi-supervised node classification, tabular data, GBDT

2020

  • Wang et al. Network Embedding with Completely-imbalanced Labels. Paper link.
    • Example code: PyTorch
    • Tags: node classification, network embedding, completely-imbalanced labels
  • Hassani and Khasahmadi. Contrastive Multi-View Representation Learning on Graphs. Paper link.
    • Example code: PyTorch
    • Tags: graph diffusion, self-supervised learning on graphs.
  • Zhu et al. Deep Graph Contrastive Representation Learning. Paper link.
    • Example code: PyTorch
    • Tags: contrastive learning for node classification.
  • Feng et al. Graph Random Neural Network for Semi-Supervised Learning on Graphs. Paper link.
    • Example code: PyTorch
    • Tags: semi-supervised node classification, simplifying graph convolution, data augmentation
  • Hu et al. Heterogeneous Graph Transformer. Paper link.
    • Example code: PyTorch
    • Tags: dynamic heterogeneous graphs, large-scale, node classification, link prediction
  • Chen. Graph Convolutional Networks for Graphs with Multi-Dimensionally Weighted Edges. Paper link.
  • Frasca et al. SIGN: Scalable Inception Graph Neural Networks. Paper link.
  • Hu et al. Strategies for Pre-training Graph Neural Networks. Paper link.
  • Marc Brockschmidt. GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation. Paper link.
    • Example code: PyTorch
    • Tags: multi-relational graphs, hypernetworks, GNN architectures
  • Li, Maosen, et al. Graph Cross Networks with Vertex Infomax Pooling. Paper link.
    • Example code: PyTorch
    • Tags: pooling, graph classification
  • Liu et al. Towards Deeper Graph Neural Networks. Paper link.
    • Example code: PyTorch
    • Tags: over-smoothing, node classification
  • Klicpera et al. Directional Message Passing for Molecular Graphs. Paper link.
    • Example code: PyTorch
    • Tags: molecules, molecular property prediction, quantum chemistry
  • Rossi et al. Temporal Graph Networks For Deep Learning on Dynamic Graphs. Paper link.
    • Example code: Pytorch
    • Tags: over-smoothing, node classification
  • Vashishth, Shikhar, et al. Composition-based Multi-Relational Graph Convolutional Networks. Paper link.
    • Example code: PyTorch
    • Tags: multi-relational graphs, graph neural network
  • Li et al. DeeperGCN: All You Need to Train Deeper GCNs. Paper link.
    • Example code: PyTorch
    • Tags: over-smoothing, deeper gnn, OGB

2019

  • Sun et al. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. Paper link.
    • Example code: PyTorch
    • Tags: semi-supervised graph regression, unsupervised graph classification
  • Bianchi et al. Graph Neural Networks with Convolutional ARMA Filters. Paper link.
    • Example code: PyTorch
    • Tags: node classification
  • Klicpera et al. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. Paper link.
  • Chiang et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. Paper link.
  • Veličković et al. Deep Graph Infomax. Paper link.
  • Ying et al. Hierarchical Graph Representation Learning with Differentiable Pooling. Paper link.
    • Example code: PyTorch
    • Tags: pooling, graph classification, graph coarsening
  • Cen et al. Representation Learning for Attributed Multiplex Heterogeneous Network. Paper link.
    • Example code: PyTorch
    • Tags: heterogeneous graphs, link prediction, large-scale
  • Xu et al. How Powerful are Graph Neural Networks? Paper link.
  • Koncel-Kedziorski et al. Text Generation from Knowledge Graphs with Graph Transformers. Paper link.
    • Example code: PyTorch
    • Tags: knowledge graph, text generation
  • Wang et al. Heterogeneous Graph Attention Network. Paper link.
    • Example code: PyTorch
    • Tags: heterogeneous graphs, node classification
  • Chen et al. Supervised Community Detection with Line Graph Neural Networks. Paper link.
    • Example code: PyTorch
    • Tags: line graph, community detection
  • Wu et al. Simplifying Graph Convolutional Networks. Paper link.
  • Wang et al. Dynamic Graph CNN for Learning on Point Clouds. Paper link.
    • Example code: PyTorch
    • Tags: point cloud classification
  • Zhang et al. Graphical Contrastive Losses for Scene Graph Parsing. Paper link.
    • Example code: MXNet
    • Tags: scene graph extraction
  • Lee et al. Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. Paper link.
  • Coley et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Paper link.
    • Example code: PyTorch
    • Tags: molecules, reaction prediction
  • Lu et al. Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective. Paper link.
    • Example code: PyTorch
    • Tags: molecules, quantum chemistry
  • Xiong et al. Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism. Paper link.
  • Sun et al. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. Paper link.
  • Abu-El-Haija et al. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Paper link.
    • Example code: PyTorch
    • Tags: node classification
  • Lee, Junhyun, et al. Self-Attention Graph Pooling. Paper link.
    • Example code: PyTorch
    • Tags: graph classification, pooling
  • Zhang, Zhen, et al. Hierarchical Graph Pooling with Structure Learning. Paper link.
    • Example code: PyTorch
    • Tags: graph classification, pooling
  • Gao, Hongyang, et al. Graph Representation Learning via Hard and Channel-Wise Attention Networks Paper link.
    • Example code: PyTorch
    • Tags: node classification, graph attention
  • Wang, Xiang, et al. Neural Graph Collaborative Filtering. Paper link.
    • Example code: PyTorch
    • Tags: Collaborative Filtering, Recommendation, Graph Neural Network
  • Ying, Rex, et al. GNNExplainer: Generating Explanations for Graph Neural Networks. Paper link.
    • Example code: PyTorch
    • Tags: Graph Neural Network, Explainability

2018

  • Li et al. Learning Deep Generative Models of Graphs. Paper link.

  • Veličković et al. Graph Attention Networks. Paper link.

  • Jin et al. Junction Tree Variational Autoencoder for Molecular Graph Generation. Paper link.

    • Example code: PyTorch
    • Tags: generative models, molecules, VAE

  • Thekumparampil et al. Attention-based Graph Neural Network for Semi-supervised Learning. Paper link.

    • Example code: PyTorch
    • Tags: node classification

  • Ying et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Paper link.

    • Example code: PyTorch
    • Tags: recommender system, large-scale, sampling

  • Berg Palm et al. Recurrent Relational Networks. Paper link.

    • Example code: PyTorch
    • Tags: sudoku solving

  • Yu et al. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Paper link.

    • Example code: PyTorch
    • Tags: spatio-temporal, traffic forecasting

  • Zhang et al. An End-to-End Deep Learning Architecture for Graph Classification. Paper link.

  • Zhang et al. Link Prediction Based on Graph Neural Networks. Paper link.

    • Example code: PyTorch
    • Tags: link prediction, sampling

  • Xu et al. Representation Learning on Graphs with Jumping Knowledge Networks. Paper link.

    • Example code: PyTorch
    • Tags: message passing, neighborhood

  • Zhang et al. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. Paper link.

    • Example code: pytorch
    • Tags: Static discrete temporal graph, traffic forcasting

2017

  • Kipf and Welling. Semi-Supervised Classification with Graph Convolutional Networks. Paper link.

  • Sabour et al. Dynamic Routing Between Capsules. Paper link.

    • Example code: PyTorch
    • Tags: image classification

  • van den Berg et al. Graph Convolutional Matrix Completion. Paper link.

    • Example code: PyTorch
    • Tags: matrix completion, recommender system, link prediction, bipartite graphs

  • Hamilton et al. Inductive Representation Learning on Large Graphs. Paper link.

  • Dong et al. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. Paper link.

    • Example code: PyTorch
    • Tags: heterogeneous graphs, network embedding, large-scale, node classification

  • Du et al. Topology Adaptive Graph Convolutional Networks. Paper link.

  • Qi et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Paper link.

    • Example code: PyTorch
    • Tags: point cloud classification, point cloud part-segmentation

  • Qi et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Paper link.

    • Example code: PyTorch
    • Tags: point cloud classification

  • Schlichtkrull. Modeling Relational Data with Graph Convolutional Networks. Paper link.

  • Vaswani et al. Attention Is All You Need. Paper link.

    • Example code: PyTorch
    • Tags: machine translation

  • Gilmer et al. Neural Message Passing for Quantum Chemistry. Paper link.

  • Gomes et al. Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity. Paper link.

    • Example code: PyTorch
    • Tags: binding affinity prediction, molecules, proteins

  • Schütt et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Paper link.

    • Example code: PyTorch
    • Tags: molecules, quantum chemistry

  • Li et al. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forcasting. Paper link.

    • Example code: Pytorch
    • Tags: Static discrete temporal graph, traffic forcasting.

2016

2015

  • Tang et al. LINE: Large-scale Information Network Embedding. Paper link.

    • Example code: PyTorch on OGB
    • Tags: network embedding, transductive learning, OGB, link prediction

  • Sheng Tai et al. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. Paper link.

    • Example code: PyTorch, MXNet
    • Tags: sentiment classification

  • Vinyals et al. Order Matters: Sequence to sequence for sets. Paper link.

  • Lin et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Paper link.

  • Yang et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. Paper link.

  • Duvenaud et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Paper link.

2014

  • Perozzi et al. DeepWalk: Online Learning of Social Representations. Paper link.

    • Example code: PyTorch on OGB
    • Tags: network embedding, transductive learning, OGB, link prediction

  • Fischer et al. A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance. Paper link.

    • Example code: PyTorch
    • Tags: graph edit distance, graph matching

2013

2011

  • Fankhauser et al. Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching. Paper link.

    • Example code: PyTorch
    • Tags: graph edit distance, graph matching

  • Nickel et al. A Three-Way Model for Collective Learning on Multi-Relational Data. Paper link.

2009

  • Riesen et al. Speeding Up Graph Edit Distance Computation with a Bipartite Heuristic. Paper link.
    • Example code: PyTorch
    • Tags: graph edit distance, graph matching

2006

  • Neuhaus et al. Fast Suboptimal Algorithms for the Computation of Graph Edit Distance. Paper link.
    • Example code: PyTorch
    • Tags: graph edit distance, graph matching

1998

  • Page et al. The PageRank Citation Ranking: Bringing Order to the Web. Paper link.
    • Example code: PyTorch
    • Tags: PageRank