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Official DGL Examples and Modules

Overview

Paper node classification link prediction / classification graph property prediction sampling OGB
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 ✔️
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 ✔️ ✔️

2020

  • 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.

2019

  • 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

2018

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

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.

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