README.md

# Graph Property Prediction for Open Graph Benchmark (OGB)

[The Open Graph Benchmark (OGB)](https://ogb.stanford.edu/) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Here we complete the Graph Property Prediction task based on PGL.

### Requirements

- paddlpaddle >= 1.7.1
- pgl 1.0.2
- ogb

NOTE: To install ogb that is fited for this project, run below command to install ogb
```
git clone https://github.com/snap-stanford/ogb.git
git checkout 482c40bc9f31fe25f9df5aa11c8fb657bd2b1621
python setup.py install
```

### How to run
For example, use GPU to train model on ogbg-molhiv dataset and ogb-molpcba dataset.
```
CUDA_VISIBLE_DEVICES=1 python -u main.py --config hiv_config.yaml --use_cuda

CUDA_VISIBLE_DEVICES=2 python -u main.py --config pcba_config.yaml --use_cuda
```

If you want to use CPU to train model, environment variables `CPU_NUM` should be specified and should be in the range of 1 to N, where N is the total CPU number on your machine.
```
CPU_NUM=1 python -u main.py --config hiv_config.yaml

CPU_NUM=1 python -u main.py --config pcba_config.yaml
```

### Experiment results

| model | hiv (rocauc)| pcba (prcauc)|
|-------|-------------|--------------|
| GIN   |0.7719 (0.0079) | 0.2232 (0.0018) |