wehub-resource-sync
165 lines
5.5 KiB
Markdown
165 lines
5.5 KiB
Markdown
# DGL Implementation of InfoGraph
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This DGL example implements the model proposed in the paper [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://arxiv.org/abs/1908.01000).
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Author's code: https://github.com/fanyun-sun/InfoGraph
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## Example Implementor
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This example was implemented by [Hengrui Zhang](https://github.com/hengruizhang98) when he was an applied scientist intern at AWS Shanghai AI Lab.
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## Dependencies
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- Python 3.7
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- PyTorch 1.7.1
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- dgl 0.6.0
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## Datasets
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##### Unsupervised Graph Classification Dataset:
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'MUTAG', 'PTC', 'IMDBBINARY'(IMDB-B), 'IMDBMULTI'(IMDB-M), 'REDDITBINARY'(RDT-B), 'REDDITMULTI5K'(RDT-M5K) of dgl.data.GINDataset.
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| Dataset | MUTAG | PTC | RDT-B | RDT-M5K | IMDB-B | IMDB-M |
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| --------------- | ----- | ----- | ------ | ------- | ------ | ------ |
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| # Graphs | 188 | 344 | 2000 | 4999 | 1000 | 1500 |
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| # Classes | 2 | 2 | 2 | 5 | 2 | 3 |
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| Avg. Graph Size | 17.93 | 14.29 | 429.63 | 508.52 | 19.77 | 13.00 |
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**Semi-supervised Graph Regression Dataset:**
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QM9 dataset for graph property prediction (regression)
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| Dataset | # Graphs | # Regression Tasks |
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| ------- | -------- | ------------------ |
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| QM9 | 130,831 | 12 |
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The 12 tasks are:
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| Keys | Description |
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| ----- | :----------------------------------------- |
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| mu | Dipole moment |
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| alpha | Isotropic polarizability |
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| homo | Highest occupied molecular orbital energ |
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| lumo | Lowest unoccupied molecular orbital energy |
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| gap | Gap between 'homo' and 'lumo' |
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| r2 | Electronic spatial extent |
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| zpve | Zero point vibrational energy |
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| U0 | Internal energy at 0K |
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| U | Internal energy at 298.15K |
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| H | Enthalpy at 298.15K |
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| G | Free energy at 298.15K |
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| Cv | Heat capavity at 298.15K |
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## Arguments
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##### Unsupervised Graph Classification:
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###### Dataset options
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```
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--dataname str The graph dataset name. Default is 'MUTAG'.
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```
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###### GPU options
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```
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--gpu int GPU index. Default is -1, using CPU.
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```
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###### Training options
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```
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--epochs int Number of training periods. Default is 20.
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--batch_size int Size of a training batch. Default is 128.
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--lr float Adam optimizer learning rate. Default is 0.01.
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--log_interval int Interval bettwen two evaluations. Default is 1.
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```
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###### Model options
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```
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--n_layers int Number of GIN layers. Default is 3.
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--hid_dim int Dimension of hidden layers. Default is 32.
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```
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##### Semi-supervised Graph Regression:
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###### Dataset options
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```
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--target str The regression Task. Default is 'mu'.
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--train_num int Number of supervised examples. Default is 5000.
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```
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###### GPU options
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```
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--gpu int GPU index. Default is -1, using CPU.
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```
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###### Training options
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```
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--epochs int Number of training periods. Default is 200.
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--batch_size int Size of a training batch. Default is 20.
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--val_batch_size int Size of a validation batch. Default is 100.
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--lr float Adam optimizer learning rate. Default is 0.001.
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```
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###### Model options
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```
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--hid_dim int Dimension of hidden layers. Default is 64.
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--reg int Regularization weight. Default is 0.001.
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```
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## How to run examples
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Training and testing unsupervised model on MUTAG.
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(As graphs in these datasets are quite small and sparse, moving graphs from cpu to gpu would take a longer time than training, we recommend using **cpu** for these datasets).
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```bash
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# MUTAG:
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python unsupervised.py --dataname MUTAG --n_layers 4 --hid_dim 32
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```
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Replace 'MUTAG' with dataname in ['MUTAG', 'PTC', 'IMDBBINARY', 'IMDBMULTI', 'REDDITBINARY', 'REDDITMULTI5K'] if you'd like to try other datasets.
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Training and testing semi-supervised model on QM9 for graph property 'mu' with gpu.
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```bash
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# QM9:
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python semisupervised.py --gpu 0 --target mu
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```
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Replace 'mu' with other target names above.
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## Performance
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The hyperparameter setting in our implementation is identical to that reported in the paper.
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##### Unsupervised Graph Classification:
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| Dataset | MUTAG | PTC | RDT-B | RDT-M5K | IMDB-B | IMDB-M |
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| :---------------: | :---: | :---: | :---: | ------- | ------ | ------ |
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| Accuracy Reported | 89.01 | 61.65 | 82.50 | 53.46 | 73.03 | 49.69 |
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| DGL | 89.88 | 63.54 | 88.50 | 56.27 | 72.70 | 50.13 |
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* REDDIT-M dataset would take a quite long time to load and evaluate.
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##### Semisupervised Graph Regression on QM9:
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Here we only provide the results of 'mu', 'alpha', 'homo'.
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| Target | mu | alpha | homo |
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| :---------------: | :----: | :----: | :----: |
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| MAE Reported | 0.3169 | 0.5444 | 0.0060 |
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| The authors' code | 0.2411 | 0.5192 | 0.1560 |
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| DGL | 0.2355 | 0.5483 | 0.1581 |
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* The source of QM9 Dataset has changed so there's a gap between the MAE reported in the paper and that we reprodcued.
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* See this [issue](https://github.com/fanyun-sun/InfoGraph/issues/8) for authors' response.
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