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...@@ -4,106 +4,7 @@ The folder contains example implementations of selected research papers related ...@@ -4,106 +4,7 @@ The folder contains example implementations of selected research papers related
* 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 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/<release_version>/examples` (E.g., https://github.com/dmlc/dgl/tree/0.5.x/examples) * For examples working with a certain release, check out `https://github.com/dmlc/dgl/tree/<release_version>/examples` (E.g., https://github.com/dmlc/dgl/tree/0.5.x/examples)
## Overview 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/).
| Paper | node classification | link prediction / classification | graph property prediction | sampling | OGB |
| ------------------------------------------------------------ | ------------------- | -------------------------------- | ------------------------- | ------------------ | ------------------ |
| [Latent Dirichlet Allocation](#lda) | :heavy_check_mark: | :heavy_check_mark: | | | |
| [Network Embedding with Completely-imbalanced Labels](#rect) | :heavy_check_mark: | | | | |
| [Learning Hierarchical Graph Neural Networks for Image Clustering](#hilander) | | | | | |
| [Boost then Convolve: Gradient Boosting Meets Graph Neural Networks](#bgnn) | :heavy_check_mark: | | | | |
| [Contrastive Multi-View Representation Learning on Graphs](#mvgrl) | :heavy_check_mark: | | :heavy_check_mark: | | |
| [Deep Graph Contrastive Representation Learning](#grace) | :heavy_check_mark: | | | | |
| [Graph Random Neural Network for Semi-Supervised Learning on Graphs](#grand) | :heavy_check_mark: | | | | |
| [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: | | |
| [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](#infograph) | | | :heavy_check_mark: | | |
| [Graph Neural Networks with convolutional ARMA filters](#arma) | :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: | | |
| [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](#gnnfilm) | :heavy_check_mark: | | | | |
| [Hierarchical Graph Pooling with Structure Learning](#hgp-sl) | | | :heavy_check_mark: | | |
| [Graph Representation Learning via Hard and Channel-Wise Attention Networks](#hardgat) |:heavy_check_mark: | | | | |
| [Neural Graph Collaborative Filtering](#ngcf) | | :heavy_check_mark: | | | |
| [Graph Cross Networks with Vertex Infomax Pooling](#gxn) | | | :heavy_check_mark: | | |
| [Towards Deeper Graph Neural Networks](#dagnn) | :heavy_check_mark: | | | | |
| [The PageRank Citation Ranking: Bringing Order to the Web](#pagerank) | | | | | |
| [Fast Suboptimal Algorithms for the Computation of Graph Edit Distance](#beam) | | | | | |
| [Speeding Up Graph Edit Distance Computation with a Bipartite Heuristic](#astar) | | | | | |
| [A Three-Way Model for Collective Learning on Multi-Relational Data](#rescal) | | | | | |
| [Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching](#bipartite) | | | | | |
| [Translating Embeddings for Modeling Multi-relational Data](#transe) | | | | | |
| [A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance](#hausdorff) | | | | | |
| [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](#distmul) | | | | | |
| [Learning Entity and Relation Embeddings for Knowledge Graph Completion](#transr) | | | | | |
| [Order Matters: Sequence to sequence for sets](#seq2seq) | | | :heavy_check_mark: | | |
| [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](#treelstm) | | | | | |
| [Complex Embeddings for Simple Link Prediction](#complex) | | | | | |
| [Gated Graph Sequence Neural Networks](#ggnn) | | | | | |
| [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](#acnn) | | | | | |
| [Attention Is All You Need](#transformer) | | | | | |
| [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](#pointnet++) | | | | | |
| [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](#pointnet) | | | | | |
| [Dynamic Routing Between Capsules](#capsule) | | | | | |
| [An End-to-End Deep Learning Architecture for Graph Classification](#dgcnn) | | | :heavy_check_mark: | | |
| [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](#stgcn) | | | | | |
| [Recurrent Relational Networks](#rrn) | | | | | |
| [Junction Tree Variational Autoencoder for Molecular Graph Generation](#jtvae) | | | | | |
| [Learning Deep Generative Models of Graphs](#dgmg) | | | | | |
| [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](#rotate) | | | | | |
| [A graph-convolutional neural network model for the prediction of chemical reactivity](#wln) | | | | | |
| [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks](#settrans) | | | :heavy_check_mark: | | |
| [Graphical Contrastive Losses for Scene Graph Parsing](#scenegraph) | | | | | |
| [Dynamic Graph CNN for Learning on Point Clouds](#dgcnnpoint) | | | | | |
| [Supervised Community Detection with Line Graph Neural Networks](#lgnn) | | | | | |
| [Text Generation from Knowledge Graphs with Graph Transformers](#graphwriter) | | | | | |
| [Temporal Graph Networks For Deep Learning on Dynamic Graphs](#tgn) | | :heavy_check_mark: | | | |
| [Directional Message Passing for Molecular Graphs](#dimenet) | | | :heavy_check_mark: | | |
| [Link Prediction Based on Graph Neural Networks](#seal) | | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: |
| [Variational Graph Auto-Encoders](#vgae) | | :heavy_check_mark: | | | |
| [Composition-based Multi-Relational Graph Convolutional Networks](#compgcn)| | :heavy_check_mark: | | | |
| [GNNExplainer: Generating Explanations for Graph Neural Networks](#gnnexplainer) | :heavy_check_mark: | | | | |
| [Interaction Networks for Learning about Objects, Relations and Physics](#graphsim) | | |:heavy_check_mark: | | |
| [Representation Learning on Graphs with Jumping Knowledge Networks](#jknet) | :heavy_check_mark: | | | | |
| [A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users](#tahin) | | :heavy_check_mark: | | | |
| [DeeperGCN: All You Need to Train Deeper GCNs](#deepergcn) | | | :heavy_check_mark: | | :heavy_check_mark: |
| [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forcasting](#dcrnn) | | | :heavy_check_mark: | | |
| [GaAN: Gated Attention Networks for Learning on large and Spatiotemporal Graphs](#gaan) | | | :heavy_check_mark: | | |
| [Combining Label Propagation and Simple Models Out-performs Graph Neural Networks](#correct_and_smooth) | :heavy_check_mark: | | | | :heavy_check_mark: |
| [Learning from Labeled and Unlabeled Data with Label Propagation](#label_propagation) | :heavy_check_mark: | | | | |
| [Heterogeneous Graph Neural Network](#hetgnn) | :heavy_check_mark: | :heavy_check_mark: | | | |
| [Graph Transformer Networks](#gtn) | :heavy_check_mark: | | | | |
| [Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](#magnn) | :heavy_check_mark: | | | | |
| [Network Schema Preserving Heterogeneous Information Network Embedding](#nshe) | :heavy_check_mark: | | | | |
## 2021 ## 2021
...@@ -464,4 +365,4 @@ The folder contains example implementations of selected research papers related ...@@ -464,4 +365,4 @@ The folder contains example implementations of selected research papers related
- <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). - <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) - Example code: [PyTorch](../examples/pytorch/pagerank.py)
- Tags: PageRank - Tags: PageRank
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