dmlc--dgl
PengZhang
8f99b13193
* feature: add a parse parameter degree_as_nlabel for pytorch-gin demo * fix some typo * [fix]: allow to benchmark all of the 9 dataset. * [Feature] add epoch number to log * [Feature]:simply list the command lines for all datasets (https://github.com/dmlc/dgl/pull/3676#discussion_r790270705) and run a test. * Update README.md Co-authored-by: Ubuntu <ubuntu@ip-172-31-10-175.ap-northeast-1.compute.internal> Co-authored-by: Mufei Li <mufeili1996@gmail.com>
84 行
3.2 KiB
Markdown
84 行
3.2 KiB
Markdown
Graph Isomorphism Network (GIN)
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============
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- Paper link: [arXiv](https://arxiv.org/abs/1810.00826) [OpenReview](https://openreview.net/forum?id=ryGs6iA5Km)
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- Author's code repo: [https://github.com/weihua916/powerful-gnns](https://github.com/weihua916/powerful-gnns).
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Dependencies
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------------
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- PyTorch 1.1.0+
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- sklearn
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- tqdm
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``bash
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pip install torch sklearn tqdm
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``
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How to run
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----------
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An experiment on the GIN in default settings can be run with
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```bash
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python main.py
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```
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An experiment on the GIN in customized settings can be run with
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```bash
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python main.py [--device 0 | --disable-cuda] --dataset COLLAB \
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--graph_pooling_type max --neighbor_pooling_type sum
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```
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add `--degree_as_nlabel` to use one-hot encodings of node degrees as node feature vectors
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Results
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-------
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results may **fluctuate**, due to random factors and the relatively small data set. if you want to follow the paper's setting, consider the script below.
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```bash
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# 4 bioinformatics datasets setting graph_pooling_type=sum, the nodes have categorical input features
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python main.py --dataset MUTAG --device 0 \
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--graph_pooling_type sum --neighbor_pooling_type sum --filename MUTAG.txt
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python main.py --dataset PTC --device 0 \
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--graph_pooling_type sum --neighbor_pooling_type sum --filename PTC.txt
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python main.py --dataset NCI1 --device 0 \
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--graph_pooling_type sum --neighbor_pooling_type sum --filename NCI1.txt
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python main.py --dataset PROTEINS --device 0 \
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--graph_pooling_type sum --neighbor_pooling_type sum --filename PROTEINS.txt
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# 5 social network datasets setting graph_pooling_type=mean, for the REDDIT datasets, we set all node feature vectors to be the same
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# (thus, features here are uninformative); for the other social networks, we use one-hot encodings of node degrees.
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python main.py --dataset COLLAB --device 0 \
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--graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename COLLAB.txt
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python main.py --dataset IMDBBINARY --device 0 \
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--graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename IMDBBINARY.txt
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python main.py --dataset IMDBMULTI --device 0 \
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--graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename IMDBMULTI.txt
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python main.py --dataset REDDITBINARY --device 0 \
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--graph_pooling_type mean --neighbor_pooling_type sum --filename REDDITBINARY.txt --fold_idx 6 --epoch 120
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python main.py --dataset REDDITMULTI5K --device 0 \
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--graph_pooling_type mean --neighbor_pooling_type sum --filename REDDITMULTI5K.txt
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```
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one fold of 10 result are below.
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| dataset | our result | paper report |
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| ------------- | ---------- | ------------ |
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| MUTAG | 89.4 | 89.4 ± 5.6 |
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| PTC | 68.5 | 64.6 ± 7.0 |
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| NCI1 | 78.5 | 82.7 ± 1.7 |
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| PROTEINS | 72.3 | 76.2 ± 2.8 |
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| COLLAB | 81.6 | 80.2 ± 1.9 |
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| IMDBBINARY | 73.0 | 75.1 ± 5.1 |
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| IMDBMULTI | 54.0 | 52.3 ± 2.8 |
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| REDDITBINARY | 88.0 | 92.4 ± 2.5 |
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| REDDITMULTI5K | 54.8 | 57.5 ± 1.5 |
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