README.md

# 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: |
| [Self-Attention Graph Pooling](#sagpool)                                                                                 |                     |                                  | :heavy_check_mark:        |                    |                    |
| [Convolutional Networks on Graphs for Learning Molecular Fingerprints](#nf)                                              |                     |                                  | :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

- <a name="sagpool"></a> 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

## 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

- <a name="nf"></a> 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

- <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