# 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. * For examples working with the latest master (or the latest [nightly build](https://www.dgl.ai/pages/start.html)), check out https://github.com/dmlc/dgl/tree/master/examples. * For examples working with a certain release, check out `https://github.com/dmlc/dgl/tree//examples` (E.g., https://github.com/dmlc/dgl/tree/0.5.x/examples) To quickly locate the examples of your interest, search for the tagged keywords or use the search tool on [dgl.ai](https://www.dgl.ai/). ## 2021 - Xing et al. Learning Hierarchical Graph Neural Networks for Image Clustering. - Example code: [PyTorch](../examples/pytorch/hilander) - Tags: clustering - Ivanov et al. Boost then Convolve: Gradient Boosting Meets Graph Neural Networks. [Paper link](https://openreview.net/forum?id=ebS5NUfoMKL). - Example code: [PyTorch](../examples/pytorch/bgnn) - Tags: semi-supervised node classification, tabular data, GBDT - Huang et al. Combining Label Propagation and Simple Models Out-performs Graph Neural Networks. [Paper link](https://arxiv.org/abs/2010.13993). - Example code: [PyTorch](../examples/pytorch/correct_and_smooth) - Tags: efficiency, node classification, label propagation - Zhao et al. Point Transformer. [Paper link](http://arxiv.org/abs/2012.09164). - Example code: [PyTorch](../examples/pytorch/pointcloud/point_transformer) - Tags: point cloud classification, point cloud part-segmentation - Guo et al. PCT: Point cloud transformer. [Paper link](http://arxiv.org/abs/2012.09688). - Example code: [PyTorch](../examples/pytorch/pointcloud/pct) - Tags: point cloud classification, point cloud part-segmentation - Brody et al. How Attentive are Graph Attention Networks? [Paper link](https://arxiv.org/abs/2105.14491). - Example code: [PyTorch](../examples/pytorch/gatv2) - Tags: graph attention, gat, gatv2, attention ## 2020 - Wagh et al. EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network. [Paper link](http://proceedings.mlr.press/v136/wagh20a.html). - Example code: [PyTorch](../examples/pytorch/eeg-gcnn) - 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](https://ieeexplore.ieee.org/document/8979355). - Example code: [PyTorch](../examples/pytorch/rect) - Tags: node classification, network embedding, completely-imbalanced labels - Hassani and Khasahmadi. Contrastive Multi-View Representation Learning on Graphs. [Paper link](https://arxiv.org/abs/2006.05582). - Example code: [PyTorch](../examples/pytorch/mvgrl) - Tags: graph diffusion, self-supervised learning - Zhu et al. Deep Graph Contrastive Representation Learning. [Paper link](https://arxiv.org/abs/2006.04131). - Example code: [PyTorch](../examples/pytorch/grace) - Tags: contrastive learning for node classification. - Feng et al. Graph Random Neural Network for Semi-Supervised Learning on Graphs. [Paper link](https://arxiv.org/abs/2005.11079). - Example code: [PyTorch](../examples/pytorch/grand) - Tags: semi-supervised node classification, simplifying graph convolution, data augmentation - Hu et al. Heterogeneous Graph Transformer. [Paper link](https://arxiv.org/abs/2003.01332). - Example code: [PyTorch](../examples/pytorch/hgt) - Tags: dynamic heterogeneous graph, large-scale, node classification, link prediction - Chen. Graph Convolutional Networks for Graphs with Multi-Dimensionally Weighted Edges. [Paper link](https://cims.nyu.edu/~chenzh/files/GCN_with_edge_weights.pdf). - Example code: [PyTorch on ogbn-proteins](../examples/pytorch/ogb/ogbn-proteins) - Tags: node classification, weighted graphs, OGB - Frasca et al. SIGN: Scalable Inception Graph Neural Networks. [Paper link](https://arxiv.org/abs/2004.11198). - Example code: [PyTorch on ogbn-arxiv/products/mag](../examples/pytorch/ogb/sign), [PyTorch](../examples/pytorch/sign) - Tags: node classification, OGB, large-scale, heterogeneous graph - Hu et al. Strategies for Pre-training Graph Neural Networks. [Paper link](https://arxiv.org/abs/1905.12265). - Example code: [Molecule embedding](https://github.com/awslabs/dgl-lifesci/tree/master/examples/molecule_embeddings), [PyTorch for custom data](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/csv_data_configuration) - Tags: molecules, graph classification, unsupervised learning, self-supervised learning, molecular property prediction - Marc Brockschmidt. GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation. [Paper link](https://arxiv.org/abs/1906.12192). - Example code: [PyTorch](../examples/pytorch/GNN-FiLM) - Tags: multi-relational graphs, hypernetworks, GNN architectures - Li, Maosen, et al. Graph Cross Networks with Vertex Infomax Pooling. [Paper link](https://arxiv.org/abs/2010.01804). - Example code: [PyTorch](../examples/pytorch/gxn) - Tags: pooling, graph classification - Liu et al. Towards Deeper Graph Neural Networks. [Paper link](https://arxiv.org/abs/2007.09296). - Example code: [PyTorch](../examples/pytorch/dagnn) - Tags: over-smoothing, node classification - Klicpera et al. Directional Message Passing for Molecular Graphs. [Paper link](https://arxiv.org/abs/2003.03123). - Example code: [PyTorch](../examples/pytorch/dimenet) - Tags: molecules, molecular property prediction, quantum chemistry - Rossi et al. Temporal Graph Networks For Deep Learning on Dynamic Graphs. [Paper link](https://arxiv.org/abs/2006.10637). - Example code: [Pytorch](../examples/pytorch/tgn) - Tags: temporal, node classification - Vashishth, Shikhar, et al. Composition-based Multi-Relational Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1911.03082). - Example code: [PyTorch](../examples/pytorch/compGCN) - Tags: multi-relational graphs, graph neural network - Li et al. DeeperGCN: All You Need to Train Deeper GCNs. [Paper link](https://arxiv.org/abs/2006.07739). - Example code: [PyTorch](../examples/pytorch/deepergcn) - 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](https://arxiv.org/abs/2007.15293). - Example code: [Pytorch](../examples/pytorch/TAHIN) - 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](https://dl.acm.org/doi/abs/10.1145/3366423.3380297). - Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/MAGNN) - Tags: Heterogeneous graph, Graph neural network, Graph embedding - Zhao J, Wang X, et al. Network Schema Preserving Heterogeneous Information Network Embedding. [Paper link](https://www.ijcai.org/Proceedings/2020/0190.pdf). - Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/NSHE) - 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](https://arxiv.org/abs/2008.08692). - Example code: [PyTorch](../examples/pytorch/caregnn) - Tags: Multi-relational graph, Graph neural network, Fraud detection, Reinforcement learning, Node classification ## 2019 - Sun et al. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. [Paper link](https://arxiv.org/abs/1908.01000). - Example code: [PyTorch](../examples/pytorch/infograph) - Tags: semi-supervised graph regression, unsupervised graph classification - Bianchi et al. Graph Neural Networks with Convolutional ARMA Filters. [Paper link](https://arxiv.org/abs/1901.01343). - Example code: [PyTorch](../examples/pytorch/arma) - Tags: node classification - Klicpera et al. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. [Paper link](https://arxiv.org/abs/1810.05997). - Example code: [PyTorch](../examples/pytorch/appnp), [MXNet](../examples/mxnet/appnp) - Tags: node classification - Chiang et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1905.07953). - Example code: [PyTorch](../examples/pytorch/cluster_gcn), [PyTorch-based GraphSAGE variant on OGB](../examples/pytorch/ogb/cluster-sage), [PyTorch-based GAT variant on OGB](../examples/pytorch/ogb/cluster-gat) - Tags: graph partition, node classification, large-scale, OGB, sampling - Veličković et al. Deep Graph Infomax. [Paper link](https://arxiv.org/abs/1809.10341). - Example code: [PyTorch](../examples/pytorch/dgi), [TensorFlow](../examples/tensorflow/dgi) - Tags: unsupervised learning, node classification - Ying et al. Hierarchical Graph Representation Learning with Differentiable Pooling. [Paper link](https://arxiv.org/abs/1806.08804). - Example code: [PyTorch](../examples/pytorch/diffpool) - Tags: pooling, graph classification, graph coarsening - Cen et al. Representation Learning for Attributed Multiplex Heterogeneous Network. [Paper link](https://arxiv.org/abs/1905.01669v2). - Example code: [PyTorch](../examples/pytorch/GATNE-T) - Tags: heterogeneous graph, link prediction, large-scale - Xu et al. How Powerful are Graph Neural Networks? [Paper link](https://arxiv.org/abs/1810.00826). - Example code: [PyTorch on graph classification](../examples/pytorch/gin), [PyTorch on node classification](../examples/pytorch/model_zoo/citation_network), [PyTorch on ogbg-ppa](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/ogbg_ppa), [MXNet](../examples/mxnet/gin) - Tags: graph classification, node classification, OGB - Koncel-Kedziorski et al. Text Generation from Knowledge Graphs with Graph Transformers. [Paper link](https://arxiv.org/abs/1904.02342). - Example code: [PyTorch](../examples/pytorch/graphwriter) - Tags: knowledge graph, text generation - Wang et al. Heterogeneous Graph Attention Network. [Paper link](https://arxiv.org/abs/1903.07293). - Example code: [PyTorch](../examples/pytorch/han), [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/HAN) - Tags: heterogeneous graph, node classification - Chen et al. Supervised Community Detection with Line Graph Neural Networks. [Paper link](https://arxiv.org/abs/1705.08415). - Example code: [PyTorch](../examples/pytorch/line_graph) - Tags: line graph, community detection - Wu et al. Simplifying Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1902.07153). - Example code: [PyTorch](../examples/pytorch/sgc), [MXNet](../examples/mxnet/sgc) - Tags: node classification - Wang et al. Dynamic Graph CNN for Learning on Point Clouds. [Paper link](https://arxiv.org/abs/1801.07829). - Example code: [PyTorch](../examples/pytorch/pointcloud/edgeconv) - Tags: point cloud classification - Zhang et al. Graphical Contrastive Losses for Scene Graph Parsing. [Paper link](https://arxiv.org/abs/1903.02728). - Example code: [MXNet](../examples/mxnet/scenegraph) - Tags: scene graph extraction - Lee et al. Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. [Paper link](https://arxiv.org/abs/1810.00825). - Pooling module: [PyTorch encoder](https://docs.dgl.ai/api/python/nn.pytorch.html#settransformerencoder), [PyTorch decoder](https://docs.dgl.ai/api/python/nn.pytorch.html#settransformerdecoder) - Tags: graph classification - Coley et al. A graph-convolutional neural network model for the prediction of chemical reactivity. [Paper link](https://pubs.rsc.org/en/content/articlelanding/2019/sc/c8sc04228d#!divAbstract). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/reaction_prediction/rexgen_direct) - Tags: molecules, reaction prediction - Lu et al. Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective. [Paper link](https://arxiv.org/abs/1906.11081). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/alchemy) - Tags: molecules, quantum chemistry - Xiong et al. Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism. [Paper link](https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b00959). - Example code: [PyTorch (with attention visualization)](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/pubchem_aromaticity), [PyTorch for custom data](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/csv_data_configuration) - Tags: molecules, molecular property prediction - Sun et al. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. [Paper link](https://arxiv.org/pdf/1902.10197.pdf). - Example code: [PyTorch](https://github.com/awslabs/dgl-ke/tree/master/examples), [PyTorch for custom data](https://aws-dglke.readthedocs.io/en/latest/commands.html) - Tags: knowledge graph - Abu-El-Haija et al. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. [Paper link](https://arxiv.org/abs/1905.00067). - Example code: [PyTorch](../examples/pytorch/mixhop) - Tags: node classification - Lee, Junhyun, et al. Self-Attention Graph Pooling. [Paper link](https://arxiv.org/abs/1904.08082). - Example code: [PyTorch](../examples/pytorch/sagpool) - Tags: graph classification, pooling - Zhang, Zhen, et al. Hierarchical Graph Pooling with Structure Learning. [Paper link](https://arxiv.org/abs/1911.05954). - Example code: [PyTorch](../examples/pytorch/hgp_sl) - Tags: graph classification, pooling - Gao, Hongyang, et al. Graph Representation Learning via Hard and Channel-Wise Attention Networks [Paper link](https://arxiv.org/abs/1907.04652). - Example code: [PyTorch](../examples/pytorch/hardgat) - Tags: node classification, graph attention - Wang, Xiang, et al. Neural Graph Collaborative Filtering. [Paper link](https://arxiv.org/abs/1905.08108). - Example code: [PyTorch](../examples/pytorch/NGCF) - Tags: Collaborative Filtering, recommender system, Graph Neural Network - Ying, Rex, et al. GNNExplainer: Generating Explanations for Graph Neural Networks. [Paper link](https://arxiv.org/abs/1903.03894). - Example code: [PyTorch](../examples/pytorch/gnn_explainer) - Tags: Graph Neural Network, Explainability - Zhang C, Song D, et al. Heterogeneous graph neural network. [Paper link](https://dl.acm.org/doi/abs/10.1145/3292500.3330961). - Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/HetGNN) - Tags: Heterogeneous graph, Graph neural network, Graph embedding - Yun S, Jeong M, et al. Graph transformer networks. [Paper link](https://arxiv.org/abs/1911.06455). - Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/GTN) - Tags: Heterogeneous graph, Graph neural network, Graph structure - Li A, Qin Z, et al. Spam Review Detection with Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1908.10679). - Example code: [PyTorch](../examples/pytorch/gas) - Tags: Fraud detection, Heterogeneous graph, Edge classification, Graph attention - Liu Z, et al. Geniepath: Graph neural networks with adaptive receptive paths. [Paper link](https://arxiv.org/abs/1802.00910). - Example code: [PyTorch](../examples/pytorch/geniepath) - Tags: Fraud detection, Node classification, Graph attention, LSTM, Adaptive receptive fields ## 2018 - Li et al. Learning Deep Generative Models of Graphs. [Paper link](https://arxiv.org/abs/1803.03324). - Example code: [PyTorch example for cycles](../examples/pytorch/dgmg), [PyTorch example for molecules](https://github.com/awslabs/dgl-lifesci/tree/master/examples/generative_models/dgmg) - Tags: generative models, autoregressive models, molecules - Veličković et al. Graph Attention Networks. [Paper link](https://arxiv.org/abs/1710.10903). - Example code: [PyTorch](../examples/pytorch/gat), [PyTorch on ogbn-arxiv](../examples/pytorch/ogb/ogbn-arxiv), [PyTorch on ogbn-products](../examples/pytorch/ogb/ogbn-products), [TensorFlow](../examples/tensorflow/gat), [MXNet](../examples/mxnet/gat) - Tags: node classification, OGB - Jin et al. Junction Tree Variational Autoencoder for Molecular Graph Generation. [Paper link](https://arxiv.org/abs/1802.04364). - Example code: [PyTorch](../examples/pytorch/jtnn) - Tags: generative models, molecules, VAE - Thekumparampil et al. Attention-based Graph Neural Network for Semi-supervised Learning. [Paper link](https://arxiv.org/abs/1803.03735). - Example code: [PyTorch](../examples/pytorch/model_zoo/citation_network) - Tags: node classification - Ying et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. [Paper link](https://arxiv.org/abs/1806.01973). - Example code: [PyTorch](../examples/pytorch/pinsage) - Tags: recommender system, large-scale, sampling - Berg Palm et al. Recurrent Relational Networks. [Paper link](https://arxiv.org/abs/1711.08028). - Example code: [PyTorch](../examples/pytorch/rrn) - Tags: sudoku solving - Yu et al. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. [Paper link](https://arxiv.org/abs/1709.04875v4). - Example code: [PyTorch](../examples/pytorch/stgcn_wave) - Tags: spatio-temporal, traffic forecasting - Zhang et al. An End-to-End Deep Learning Architecture for Graph Classification. [Paper link](https://www.cse.wustl.edu/~ychen/public/DGCNN.pdf). - Pooling module: [PyTorch](https://docs.dgl.ai/api/python/nn.pytorch.html#sortpooling), [TensorFlow](https://docs.dgl.ai/api/python/nn.tensorflow.html#sortpooling), [MXNet](https://docs.dgl.ai/api/python/nn.mxnet.html#sortpooling) - Tags: graph classification - Zhang et al. Link Prediction Based on Graph Neural Networks. [Paper link](https://papers.nips.cc/paper/2018/file/53f0d7c537d99b3824f0f99d62ea2428-Paper.pdf). - Example code: [PyTorch](../examples/pytorch/seal) - Tags: link prediction, sampling - Xu et al. Representation Learning on Graphs with Jumping Knowledge Networks. [Paper link](https://arxiv.org/abs/1806.03536). - Example code: [PyTorch](../examples/pytorch/jknet) - Tags: message passing, neighborhood - Zhang et al. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. [Paper link](https://arxiv.org/abs/1803.07294). - Example code: [pytorch](../examples/pytorch/dtgrnn) - Tags: Static discrete temporal graph, traffic forecasting ## 2017 - Kipf and Welling. Semi-Supervised Classification with Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1609.02907). - Example code: [PyTorch](../examples/pytorch/gcn), [PyTorch on ogbn-arxiv](../examples/pytorch/ogb/ogbn-arxiv), [PyTorch on ogbl-ppa](https://github.com/awslabs/dgl-lifesci/tree/master/examples/link_prediction/ogbl-ppa), [PyTorch on ogbg-ppa](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/ogbg_ppa), [TensorFlow](../examples/tensorflow/gcn), [MXNet](../examples/mxnet/gcn) - Tags: node classification, link prediction, graph classification, OGB - Sabour et al. Dynamic Routing Between Capsules. [Paper link](https://arxiv.org/abs/1710.09829). - Example code: [PyTorch](../examples/pytorch/capsule) - Tags: image classification - van den Berg et al. Graph Convolutional Matrix Completion. [Paper link](https://arxiv.org/abs/1706.02263). - Example code: [PyTorch](../examples/pytorch/gcmc) - Tags: matrix completion, recommender system, link prediction, bipartite graphs - Hamilton et al. Inductive Representation Learning on Large Graphs. [Paper link](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf). - Example code: [PyTorch](../examples/pytorch/graphsage), [PyTorch on ogbn-products](../examples/pytorch/ogb/ogbn-products), [PyTorch on ogbl-ppa](https://github.com/awslabs/dgl-lifesci/tree/master/examples/link_prediction/ogbl-ppa), [MXNet](../examples/mxnet/graphsage) - Tags: node classification, sampling, unsupervised learning, link prediction, OGB - Dong et al. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. [Paper link](https://dl.acm.org/doi/10.1145/3097983.3098036). - Example code: [PyTorch](../examples/pytorch/metapath2vec) - Tags: heterogeneous graph, network embedding, large-scale, node classification - Du et al. Topology Adaptive Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1710.10370). - Example code: [PyTorch](../examples/pytorch/tagcn), [MXNet](../examples/mxnet/tagcn) - Tags: node classification - Qi et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [Paper link](https://arxiv.org/abs/1612.00593). - Example code: [PyTorch](../examples/pytorch/pointcloud/pointnet) - 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](https://arxiv.org/abs/1706.02413). - Example code: [PyTorch](../examples/pytorch/pointcloud/pointnet) - Tags: point cloud classification - Schlichtkrull. Modeling Relational Data with Graph Convolutional Networks. [Paper link](https://arxiv.org/abs/1703.06103). - Example code: [PyTorch example using homogeneous DGLGraphs](../examples/pytorch/rgcn), [PyTorch](../examples/pytorch/rgcn-hetero), [TensorFlow](../examples/tensorflow/rgcn), [MXNet](../examples/mxnet/rgcn) - Tags: node classification, link prediction, heterogeneous graph, sampling - Vaswani et al. Attention Is All You Need. [Paper link](https://arxiv.org/abs/1706.03762). - Example code: [PyTorch](../examples/pytorch/transformer) - Tags: machine translation - Gilmer et al. Neural Message Passing for Quantum Chemistry. [Paper link](https://arxiv.org/abs/1704.01212). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/alchemy), [PyTorch for custom data](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/csv_data_configuration) - Tags: molecules, quantum chemistry - Gomes et al. Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity. [Paper link](https://arxiv.org/abs/1703.10603). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/binding_affinity_prediction) - Tags: binding affinity prediction, molecules, proteins - Schütt et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. [Paper link](https://arxiv.org/abs/1706.08566). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/alchemy) - Tags: molecules, quantum chemistry - Li et al. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forcasting. [Paper link](https://arxiv.org/abs/1707.01926). - Example code: [Pytorch](../examples/pytorch/dtgrnn) - Tags: Static discrete temporal graph, traffic forecasting ## 2016 - Li et al. Gated Graph Sequence Neural Networks. [Paper link](https://arxiv.org/abs/1511.05493). - Example code: [PyTorch](../examples/pytorch/ggnn) - Tags: question answering - Defferrard et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. [Paper link](https://arxiv.org/abs/1606.09375). - Example code: [PyTorch on image classification](../examples/pytorch/model_zoo/geometric), [PyTorch on node classification](../examples/pytorch/model_zoo/citation_network) - Tags: image classification, graph classification, node classification - Monti et al. Geometric deep learning on graphs and manifolds using mixture model CNNs. [Paper link](https://arxiv.org/abs/1611.08402). - Example code: [PyTorch on image classification](../examples/pytorch/model_zoo/geometric), [PyTorch on node classification](../examples/pytorch/monet), [MXNet on node classification](../examples/mxnet/monet) - Tags: image classification, graph classification, node classification - Kearnes et al. Molecular Graph Convolutions: Moving Beyond Fingerprints. [Paper link](https://arxiv.org/abs/1603.00856). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/moleculenet), [PyTorch for custom data](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/csv_data_configuration) - Tags: molecular property prediction - Trouillon et al. Complex Embeddings for Simple Link Prediction. [Paper link](http://proceedings.mlr.press/v48/trouillon16.pdf). - Example code: [PyTorch](https://github.com/awslabs/dgl-ke/tree/master/examples), [PyTorch for custom data](https://aws-dglke.readthedocs.io/en/latest/commands.html) - Tags: knowledge graph - Thomas et al. Variational Graph Auto-Encoders. [Paper link](https://arxiv.org/abs/1611.07308). - Example code: [PyTorch](../examples/pytorch/vgae) - Tags: link prediction ## 2015 - Tang et al. LINE: Large-scale Information Network Embedding. [Paper link](https://arxiv.org/abs/1503.03578). - Example code: [PyTorch on OGB](../examples/pytorch/ogb/line) - 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](https://arxiv.org/abs/1503.00075). - Example code: [PyTorch](../examples/pytorch/tree_lstm), [MXNet](../examples/mxnet/tree_lstm) - Tags: sentiment classification - Vinyals et al. Order Matters: Sequence to sequence for sets. [Paper link](https://arxiv.org/abs/1511.06391). - Pooling module: [PyTorch](https://docs.dgl.ai/api/python/nn.pytorch.html#set2set), [MXNet](https://docs.dgl.ai/api/python/nn.mxnet.html#set2set) - Tags: graph classification - Lin et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. [Paper link](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewPaper/9571). - Example code: [PyTorch](https://github.com/awslabs/dgl-ke/tree/master/examples), [PyTorch for custom data](https://aws-dglke.readthedocs.io/en/latest/commands.html) - Tags: knowledge graph - Yang et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. [Paper link](https://arxiv.org/abs/1412.6575). - Example code: [PyTorch](https://github.com/awslabs/dgl-ke/tree/master/examples), [PyTorch for custom data](https://aws-dglke.readthedocs.io/en/latest/commands.html) - Tags: knowledge graph - Duvenaud et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints. [Paper link](https://arxiv.org/abs/1509.09292). - Example code: [PyTorch](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/moleculenet), [PyTorch for custom data](https://github.com/awslabs/dgl-lifesci/tree/master/examples/property_prediction/csv_data_configuration) - Tags: molecules, molecular property prediction ## 2014 - Perozzi et al. DeepWalk: Online Learning of Social Representations. [Paper link](https://arxiv.org/abs/1403.6652). - Example code: [PyTorch on OGB](../examples/pytorch/ogb/deepwalk) - Tags: network embedding, transductive learning, OGB, link prediction - Fischer et al. A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance. [Paper link](https://link.springer.com/chapter/10.1007/978-3-662-44415-3_9). - Example code: [PyTorch](../examples/pytorch/graph_matching) - Tags: graph edit distance, graph matching ## 2013 - Bordes et al. Translating Embeddings for Modeling Multi-relational Data. [Paper link](https://proceedings.neurips.cc/paper/2013/file/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf). - Example code: [PyTorch](https://github.com/awslabs/dgl-ke/tree/master/examples), [PyTorch for custom data](https://aws-dglke.readthedocs.io/en/latest/commands.html) - Tags: knowledge graph ## 2011 - Fankhauser et al. Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching. [Paper link](https://link.springer.com/chapter/10.1007/978-3-642-20844-7_11). - Example code: [PyTorch](../examples/pytorch/graph_matching) - Tags: graph edit distance, graph matching - Nickel et al. A Three-Way Model for Collective Learning on Multi-Relational Data. [Paper link](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.2015&rep=rep1&type=pdf). - Example code: [PyTorch](https://github.com/awslabs/dgl-ke/tree/master/examples), [PyTorch for custom data](https://aws-dglke.readthedocs.io/en/latest/commands.html) - Tags: knowledge graph ## 2010 - Hoffman et al. Online Learning for Latent Dirichlet Allocation. [Paper link](https://papers.nips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf). - Example code: [PyTorch](../examples/pytorch/lda) - Tags: sklearn, decomposition, latent Dirichlet allocation ## 2009 - Riesen et al. Speeding Up Graph Edit Distance Computation with a Bipartite Heuristic. [Paper link](https://core.ac.uk/download/pdf/33054885.pdf). - Example code: [PyTorch](../examples/pytorch/graph_matching) - Tags: graph edit distance, graph matching ## 2006 - Neuhaus et al. Fast Suboptimal Algorithms for the Computation of Graph Edit Distance. [Paper link](https://link.springer.com/chapter/10.1007/11815921_17). - Example code: [PyTorch](../examples/pytorch/graph_matching) - Tags: graph edit distance, graph matching ## 2002 - Zhu & Ghahramani. Learning from Labeled and Unlabeled Data with Label Propagation. [Paper link](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.3864&rep=rep1&type=pdf). - Example code: [PyTorch](../examples/pytorch/label_propagation) - Tags: node classification, label propagation ## 1998 - Page et al. The PageRank Citation Ranking: Bringing Order to the Web. [Paper link](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5427). - Example code: [PyTorch](../examples/pytorch/pagerank.py) - Tags: PageRank