dmlc--dgl
Quan (Andy) Gan
68 行
2.1 KiB
Markdown
68 行
2.1 KiB
Markdown
Representation Learning for Attributed Multiplex Heterogeneous Network (GANTE)
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============
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- Paper link: [https://arxiv.org/abs/1905.01669](https://arxiv.org/abs/1905.01669)
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- Author's code repo: [https://github.com/THUDM/GATNE](https://github.com/THUDM/GATNE). Note that only GATNE-T is implemented here.
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Requirements
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------------
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- requirements
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```bash
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pip install -r requirements.txt
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```
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Also requires PyTorch 1.7.0+.
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Datasets
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--------
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To prepare the datasets:
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1. ```bash
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mkdir data
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cd data
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```
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2. Download datasets from the following links:
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- example: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/example.zip
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- amazon: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/amazon.zip
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- youtube: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/youtube.zip
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- twitter: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/twitter.zip
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3. Unzip the datasets
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Training
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--------
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Run with following (available dataset: "example", "youtube", "amazon")
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```bash
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python src/main.py --input data/example
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```
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To run on "twitter" dataset, use
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```bash
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python src/main.py --input data/twitter --eval-type 1 --gpu 0
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```
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For a big dataset, use sparse to avoid cuda out of memory in backward
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```bash
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python src/main_sparse.py --input data/example --gpu 0
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```
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If you have multiple GPUs, you can also accelerate training with [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html)
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```bash
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python src/main_sparse_multi_gpus.py --input data/example --gpu 0,1
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```
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**It is worth noting that DistributedDataParallel will cause more cuda memory consumption and a certain loss of preformance.**
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Results
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-------
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All the results match the [official code](https://github.com/THUDM/GATNE/blob/master/src/main_pytorch.py) with the same hyper parameter values, including twiiter dataset (auc, pr, f1 is 76.29, 76.17, 69.34, respectively).
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| | auc | pr | f1 |
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| ------- | ----- | ----- | ----- |
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| amazon | 96.88 | 96.31 | 92.12 |
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| youtube | 82.29 | 80.35 | 74.63 |
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| twitter | 72.40 | 74.40 | 65.89 |
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| example | 94.65 | 94.57 | 89.99 |
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