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README.md
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[![Benchmark by ASV](http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat)](https://asv.dgl.ai/) [![Benchmark by ASV](http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat)](https://asv.dgl.ai/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)
Documentation ([Latest](https://docs.dgl.ai/en/latest/) | [Stable](https://docs.dgl.ai)) | [DGL at a glance](https://docs.dgl.ai/tutorials/basics/1_first.html#sphx-glr-tutorials-basics-1-first-py) | [Model Tutorials](https://docs.dgl.ai/tutorials/models/index.html) | [Discussion Forum](https://discuss.dgl.ai) | [Slack Channel](https://join.slack.com/t/deep-graph-library/shared_invite/zt-eb4ict1g-xcg3PhZAFAB8p6dtKuP6xQ) Documentation ([Latest](https://docs.dgl.ai/en/latest/) | [Stable](https://docs.dgl.ai)) | [DGL at a glance](https://docs.dgl.ai/tutorials/basics/1_first.html#sphx-glr-tutorials-basics-1-first-py) | [Model Tutorials](https://docs.dgl.ai/tutorials/models/index.html) | [Official Examples](examples/README.md) | [Discussion Forum](https://discuss.dgl.ai) | [Slack Channel](https://join.slack.com/t/deep-graph-library/shared_invite/zt-eb4ict1g-xcg3PhZAFAB8p6dtKuP6xQ)
**For a full list of official DGL examples, see [here](examples).**
DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow. DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.
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# Official DGL Examples and Modules
## Overview
| Paper | node classification | link prediction / classification | graph property prediction | sampling | OGB |
| ------------------------------------------------------------------------------------------------------------------------ | ------------------- | -------------------------------- | ------------------------- | ------------------ | ------------------ |
| [Heterogeneous Graph Transformer](#hgt) | :heavy_check_mark: | :heavy_check_mark: | | | |
| [Graph Convolutional Networks for Graphs with Multi-Dimensionally Weighted Edges](#mwe) | :heavy_check_mark: | | | | :heavy_check_mark: |
| [SIGN: Scalable Inception Graph Neural Networks](#sign) | :heavy_check_mark: | | | | :heavy_check_mark: |
| [Strategies for Pre-training Graph Neural Networks](#prestrategy) | | | :heavy_check_mark: | | |
| [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](#appnp) | :heavy_check_mark: | | | | |
| [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](#clustergcn) | :heavy_check_mark: | | | :heavy_check_mark: | :heavy_check_mark: |
| [Deep Graph Infomax](#dgi) | :heavy_check_mark: | | | | |
| [Hierarchical Graph Representation Learning with Differentiable Pooling](#diffpool) | | | :heavy_check_mark: | | |
| [Representation Learning for Attributed Multiplex Heterogeneous Network](#gatne-t) | | :heavy_check_mark: | | | |
| [How Powerful are Graph Neural Networks?](#gin) | :heavy_check_mark: | | :heavy_check_mark: | | :heavy_check_mark: |
| [Heterogeneous Graph Attention Network](#han) | :heavy_check_mark: | | | | |
| [Simplifying Graph Convolutional Networks](#sgc) | :heavy_check_mark: | | | | |
| [Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective](#mgcn) | | | :heavy_check_mark: | | |
| [Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism](#attentivefp) | | | :heavy_check_mark: | | |
| [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](#mixhop) | :heavy_check_mark: | | | | |
| [Graph Attention Networks](#gat) | :heavy_check_mark: | | | | :heavy_check_mark: |
| [Attention-based Graph Neural Network for Semi-supervised Learning](#agnn) | :heavy_check_mark: | | | :heavy_check_mark: | |
| [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](#pinsage) | | | | | |
| [Semi-Supervised Classification with Graph Convolutional Networks](#gcn) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
| [Graph Convolutional Matrix Completion](#gcmc) | | :heavy_check_mark: | | | |
| [Inductive Representation Learning on Large Graphs](#graphsage) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: |
| [metapath2vec: Scalable Representation Learning for Heterogeneous Networks](#metapath2vec) | :heavy_check_mark: | | | | |
| [Topology Adaptive Graph Convolutional Networks](#tagcn) | :heavy_check_mark: | | | | |
| [Modeling Relational Data with Graph Convolutional Networks](#rgcn) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | |
| [Neural Message Passing for Quantum Chemistry](#mpnn) | | | :heavy_check_mark: | | |
| [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](#schnet) | | | :heavy_check_mark: | | |
| [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](#chebnet) | :heavy_check_mark: | | :heavy_check_mark: | | |
| [Geometric deep learning on graphs and manifolds using mixture model CNNs](#monet) | :heavy_check_mark: | | :heavy_check_mark: | | |
| [Molecular Graph Convolutions: Moving Beyond Fingerprints](#weave) | | | :heavy_check_mark: | | |
| [LINE: Large-scale Information Network Embedding](#line) | | :heavy_check_mark: | | | :heavy_check_mark: |
| [DeepWalk: Online Learning of Social Representations](#deepwalk) | | :heavy_check_mark: | | | :heavy_check_mark: |
| | | | | | |
| | | | | | |
## 2020
- <a name="hgt"></a> Hu et al. Heterogeneous Graph Transformer. [Paper link](https://arxiv.org/abs/2003.01332).
- Example code: [PyTorch](../examples/pytorch/hgt)
- Tags: dynamic heterogeneous graphs, large-scale, node classification, link prediction
- <a name="mwe"></a> 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
- <a name="sign"></a> 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 graphs
- <a name="prestrategy"></a> 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
## 2019
- <a name="appnp"></a> 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
- <a name="clustergcn"></a> 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
- <a name="dgi"></a> 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
- <a name="diffpool"></a> 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
- <a name="gatne-t"></a> 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 graphs, link prediction, large-scale
- <a name="gin"></a> 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
- <a name="graphwriter"></a> 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
- <a name="han"></a> Wang et al. Heterogeneous Graph Attention Network. [Paper link](https://arxiv.org/abs/1903.07293).
- Example code: [PyTorch](../examples/pytorch/han)
- Tags: heterogeneous graphs, node classification
- <a name="lgnn"></a> 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
- <a name="sgc"></a> 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
- <a name="dgcnnpoint"></a> 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
- <a name="scenegraph"></a> 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
- <a name="settrans"></a> 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
- <a name="wln"></a> 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
- <a name="mgcn"></a> 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
- <a name="attentivefp"></a> 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
- <a name="rotate"></a> 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 embedding
- <a name="mixhop"></a> 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
## 2018
- <a name="dgmg"></a> 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
- <a name="gat"></a> 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
- <a name="jtvae"></a> 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
- <a name="agnn"></a> 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
- <a name="pinsage"></a> 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
- <a name="rrn"></a> Berg Palm et al. Recurrent Relational Networks. [Paper link](https://arxiv.org/abs/1711.08028).
- Example code: [PyTorch](../examples/pytorch/rrn)
- Tags: sudoku solving
- <a name="stgcn"></a> 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
- <a name="dgcnn"></a> 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
## 2017
- <a name="gcn"></a> 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
- <a name="capsule"></a> Sabour et al. Dynamic Routing Between Capsules. [Paper link](https://arxiv.org/abs/1710.09829).
- Example code: [PyTorch](../examples/pytorch/capsule)
- Tags: image classification
- <a name="gcmc"></a> 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
- <a name="graphsage"></a> 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
- <a name="metapath2vec"></a> 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 graphs, network embedding, large-scale, node classification
- <a name="tagcn"></a> 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
- <a name="pointnet"></a> 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
- <a name="pointnet++"></a> 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
- <a name="rgcn"></a> 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 graphs, sampling
- <a name="transformer"></a> 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
- <a name="mpnn"></a> 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
- <a name="acnn"></a> 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
- <a name="schnet"></a> 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
## 2016
- <a name="ggnn"></a> 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
- <a name="chebnet"></a> 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
- <a name="monet"></a> 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
- <a name="weave"></a> 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
- <a name="complex"></a> 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 embedding
## 2015
- <a name="line"></a> 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
- <a name="treelstm"></a> 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
- <a name="seq2seq"></a> 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
- <a name="transr"></a> 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 embedding
- <a name="distmul"></a> 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 embedding
## 2014
- <a name="deepwalk"></a> 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
- <a name="hausdorff"></a> 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
- <a name="transe"></a> 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 embedding
## 2011
- <a name="bipartite"></a> 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
- <a name="rescal"></a> 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 embedding
## 2009
- <a name="astar"></a> 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
- <a name="beam"></a> 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
## 1998
- <a name="pagerank"></a> 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
examples/pytorch/README.md deleted 100644 → 0
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# Model Examples using DGL (w/ Pytorch backend)
Each model is hosted in their own folders. Please read their README.md to see how to
run them.
To understand step-by-step how these models are implemented in DGL. Check out our
[tutorials](https://docs.dgl.ai/tutorials/models/index.html)
## Model summary
Here is a summary of the model accuracy and training speed. Our testbed is Amazon EC2 p3.2x instance (w/ V100 GPU).
| Model | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time) | DGL speed (epoch time) | Improvement |
| ----- | ----------------- | ------------ | ------------------------------------ | ---------------------- | ----------- |
| [GCN](https://arxiv.org/abs/1609.02907) | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0031s | 1.64x |
| [GAT](https://arxiv.org/abs/1710.10903) | 83.0% | 83.9% | [0.0982s (TF)](https://github.com/PetarV-/GAT) | 0.0113s | 8.69x |
| [SGC](https://arxiv.org/abs/1902.07153) | 81.0% | 81.9% | n/a | 0.0008s | n/a |
| [TreeLSTM](http://arxiv.org/abs/1503.00075) | 51.0% | 51.72% | [14.02s (DyNet)](https://github.com/clab/dynet/tree/master/examples/treelstm) | 3.18s | 4.3x |
| [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103) | 73.23% | 73.53% | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn) | 0.0075s | 38.2x |
| [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158 | 0.151 | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction) | 0.453s | 4.86x |
| [JTNN](https://arxiv.org/abs/1802.04364) | 96.44% | 96.44% | [1826s (Pytorch)](https://github.com/wengong-jin/icml18-jtnn) | 743s | 2.5x |
| [LGNN](https://arxiv.org/abs/1705.08415) | 94% | 94% | n/a | 1.45s | n/a |
| [DGMG](https://arxiv.org/pdf/1803.03324.pdf) | 84% | 90% | n/a | 238s | n/a |
| [GraphWriter](https://www.aclweb.org/anthology/N19-1238.pdf) | 14.31(BLEU) | 14.3(BLEU) | 1970s | 1192s | 1.65x |
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