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Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on pytorch_geometric
| Date | Stars |
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| 2026-07-24 | 927 |
| 2026-07-25 | 927 |
| 2026-07-28 | 927 |
| 2026-07-30 | 927 |
| 2026-08-06 | 927 |
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# Heterogeneous Graph Transformer (HGT) [Alternative reference Deep Graph Library (DGL) implementation](https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgt) Heterogeneous Graph Transformer is a graph neural network architecture that can deal with large-scale heterogeneous and dynamic graphs. You can see our WWW 2020 paper [“**Heterogeneous Graph Transformer**”](https://arxiv.org/abs/2003.01332) for more details. This implementation of HGT is based on [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric) API ## Overview The most important files in this projects are as follow: - conv.py: The core of our model, implements the transformer-like heterogeneous graph convolutional layer. - model.py: The wrap of different model components. - data.py: The data interface and usage. - `class Graph`: The data structure of heterogeneous graph. Stores feature in ``Graph.node_feature`` as pandas.DataFrame; Stores adjacency matrix in ``Graph.edge_list`` as dictionay. - `def sample_subgraph`: The sampling algorithm for heterogeneous graph. Each iteration samples a fixed number of nodes per type. All the sampled nodes are within the region of already sampled nodes, with sampling probability as the square of relative degree. - train_*.py: The training and validation script for a specific downstream task. - `def *_sample`: The sampling function for a given task. Remember to mask out existing link within the graph to avoid information leakage. - `def prepare_data`: Conduct sampling in parallel with multiple processes, which can seamlessly coordinate with model training. ## Setup This implementation is based on pytorch_geometric. To run the code, you need the following dependencies: - [Pytorch 1.3.0](https://pytorch.org/) - [pytorch_geometric 1.3.2](https://pytorch-geometric.readthedocs.io/) - torch-cluster==1.4.5 - torch-scatter==1.3.2 - torch-sparse==0.4.3 - [gensim](https://github.com/RaRe-Technologies/gensim) - [sklearn](https://github.com/scikit-learn/scikit-learn) - [tqdm](https://github.com/tqdm/tqdm) - [dill](https://github.com/uqfoundation/dill) - [pandas](https://github.com/pandas-dev/pandas) You can simply run ```pip install -r requirements.txt``` to install all the necessary packages. ## OAG DataSet Our current experiments are conducted on Open Academic Graph (OAG). For easiness of usage, we split and preprocess the whole dataset into different granularity: all **CS papers (8.1G), all ML papers (1.9G), all NN papers (0.6G)** spanning from 1900-2020. You can download the preprocessed graph via this [link](https://drive.google.com/open?id=1a85skqsMBwnJ151QpurLFSa9o2ymc_rq). If you want to directly process from raw data, you can download via this [link](https://drive.google.com/open?id=1yDdVaartOCOSsQlUZs8cJcAUhmvRiBSz). After downloading it, run `preprocess_OAG.py` to extract features and store them in our data structure. You can also use our code to process other heterogeneous graph, as long as you load them into our data structure `class Graph` in data.py. Refer to preprocess_OAG.py for a demonstration. ## Usage Execute the following scripts to train on paper-field (L2) classification task using HGT: ```bash python3 train_paper_field.py --data_dir PATH_OF_DATASET --model_dir PATH_OF_SAVED_MODEL --conv_name hgt ``` Conducting other two tasks are similar. There are some key options of this scrips: - `conv_name`: Choose corresponding model for training. By default we use HGT. - `--sample_depth` and `--sample_width`: The depth and width of sampled graph. If the model exceeds the GPU memory, can consider reduce their number; if one wants to train a deeper GNN model, consider adding these numbers. - `--n_pool`: The number of process to parallely conduct sampling. If one has a machine with large memory, can consider adding this number to reduce batch prepartion time. - `--repeat`: The number of time to reuse a sampled batch for training. If the training time is much smaller than samplin
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