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dgl/examples
peizhou001 ce37a9349a
[Temporal] Add link prediction example (#7347) 		3 days ago
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advanced/cugraph [Model] Add `dgl.nn.CuGraphSAGEConv` model (#5137) 1 year ago
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mxnet Fix num_labels to num_classes in dataset files (#6666) 4 months ago
pytorch [Misc] Fix validation dataset's usage in RGCN link example (#7308) 2 weeks ago
sampling [Temporal] Add link prediction example (#7347) 3 days ago
sparse [Sparse] Add a LADIES example (#6560) 5 months ago
tensorflow Fix num_labels to num_classes in dataset files (#6666) 4 months ago
README.md [Feature] ARGO: an easy-to-use runtime to improve GNN training performance on multi-core processors (#7003) 2 months ago

README.md

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.

To quickly locate the examples of your interest, search for the tagged keywords or use the search tool on dgl.ai.

2024

  • Lin et al. ARGO: An Auto-Tuning Runtime System for Scalable GNN Training on Multi-Core Processor. Paper link

    • Example code: PyTorch

    • Tags: semi-supervised node classification

2023

  • Zheng Wang et al. From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited. Paper link

    • Example code: PyTorch

    • Tags: semi-supervised node classification

2022

  • Balin et al. Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs. Paper link
    • Example code: PyTorch
    • Tags: node classification, weighted graphs, sampling

2021

  • Mallet et al. Learning Protein and Small Molecule binding sites in RNA molecules with 2.5D graphs. Paper link
    • Example code: PyTorch
    • Tags: semi-supervised node classification
  • Xing et al. Learning Hierarchical Graph Neural Networks for Image Clustering.
    • Example code: PyTorch
    • Tags: clustering
  • 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
  • Huang et al. Combining Label Propagation and Simple Models Out-performs Graph Neural Networks. Paper link.
    • Example code: PyTorch
    • Tags: efficiency, node classification, label propagation
  • Zhao et al. Point Transformer. Paper link.
    • Example code: PyTorch
    • Tags: point cloud classification, point cloud part-segmentation
  • Guo et al. PCT: Point cloud transformer. Paper link.
    • Example code: PyTorch
    • Tags: point cloud classification, point cloud part-segmentation
  • Brody et al. How Attentive are Graph Attention Networks? Paper link.
    • Example code: PyTorch
    • Tags: graph attention, gat, gatv2, attention
  • Thakoor et al. Large-Scale Representation Learning on Graphs via Bootstrapping. Paper link.
    • Example code: PyTorch
    • Tags: contrastive learning for node classification.
  • Bouritsas et al. Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. Paper link.
    • Example code: PyTorch
    • Tags: subgraph isomorphism counting, graph classification.
  • Song et al. Network In Graph Neural Network. Paper link.
    • Example code: PyTorch
    • Tags: model-agnostic methodology, link prediction, open graph benchmark.
  • Qin et al. BiPointNet: Binary Neural Network for Point Clouds. Paper link
    • Example code: PyTorch
    • Tags: point cloud classification, network binarization.

2020

  • Wagh et al. EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network. Paper link.

    • Example code: PyTorch
    • Tags: graph classification, eeg representation learning, brain activity, graph convolution, neurological disease classification, large dataset, edge weights, node features, fully-connected graph, graph neural network

  • 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

  • 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 graph, 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: temporal, 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

  • Bi, Ye, et al. A Heterogeneous Information Network based Cross DomainInsurance Recommendation System for Cold Start Users. Paper link.

    • Example code: Pytorch
    • Tags: cross-domain recommendation, graph neural network

  • Fu X, Zhang J, Meng Z, et al. MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. Paper link.

    • Example code: OpenHGNN
    • Tags: Heterogeneous graph, Graph neural network, Graph embedding

  • Zhao J, Wang X, et al. Network Schema Preserving Heterogeneous Information Network Embedding. Paper link.

    • Example code: OpenHGNN
    • Tags: Heterogeneous graph, Graph neural network, Graph embedding, Network Schema

  • Dou Y, Liu Z, et al. Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. Paper link.

    • Example code: PyTorch
    • Tags: Multi-relational graph, Graph neural network, Fraud detection, Reinforcement learning, Node classification

  • Zhang et al. Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. Paper link.

    • Example code: PyTorch
    • Tags: link prediction, labeling trick, 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 graph, 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, OpenHGNN
    • Tags: heterogeneous graph, 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, recommender system, Graph Neural Network
  • Ying, Rex, et al. GNNExplainer: Generating Explanations for Graph Neural Networks. Paper link.
    • Example code: PyTorch
    • Tags: Graph Neural Network, Explainability
  • Zhang C, Song D, et al. Heterogeneous graph neural network. Paper link.
    • Example code: OpenHGNN
    • Tags: Heterogeneous graph, Graph neural network, Graph embedding
  • Yun S, Jeong M, et al. Graph transformer networks. Paper link.
    • Example code: OpenHGNN
    • Tags: Heterogeneous graph, Graph neural network, Graph structure
  • Li A, Qin Z, et al. Spam Review Detection with Graph Convolutional Networks. Paper link.
    • Example code: PyTorch
    • Tags: Fraud detection, Heterogeneous graph, Edge classification, Graph attention
  • Liu Z, et al. Geniepath: Graph neural networks with adaptive receptive paths. Paper link.
    • Example code: PyTorch
    • Tags: Fraud detection, Node classification, Graph attention, LSTM, Adaptive receptive fields
  • You J, et al. Position-aware graph neural networks. Paper link.
    • Example code: PyTorch
    • Tags: Positional encoding, Link prediction, Link-pair prediction

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 forecasting

  • Feng et al. Hypergraph Neural Networks. Paper link.

    • Example code: pytorch
    • Tags: hypergraph

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 graph, 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 forecasting

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.

2010

  • Hoffman et al. Online Learning for Latent Dirichlet Allocation. Paper link.
    • Example code: PyTorch
    • Tags: sklearn, decomposition, latent Dirichlet allocation

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

2002

  • Zhu & Ghahramani. Learning from Labeled and Unlabeled Data with Label Propagation. Paper link.
    • Example code: PyTorch
    • Tags: node classification, label propagation

1998

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