wehub-resource-sync
93 lines
5.1 KiB
Markdown
93 lines
5.1 KiB
Markdown
# DGL Implementation of the HGP-SL Paper
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This DGL example implements the GNN model proposed in the paper [Hierarchical Graph Pooling with Structure Learning](https://arxiv.org/pdf/1911.05954.pdf).
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The author's codes of implementation is in [here](https://github.com/cszhangzhen/HGP-SL)
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Example implementor
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----------------------
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This example was implemented by [Tianqi Zhang](https://github.com/lygztq) during his Applied Scientist Intern work at the AWS Shanghai AI Lab.
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The graph dataset used in this example
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The DGL's built-in [LegacyTUDataset](https://docs.dgl.ai/api/python/dgl.data.html?highlight=tudataset#dgl.data.LegacyTUDataset). This is a serial of graph kernel datasets for graph classification. We use 'DD', 'PROTEINS', 'NCI1', 'NCI109', 'Mutagenicity' and 'ENZYMES' in this HGP-SL implementation. All these datasets are randomly splited to train, validation and test set with ratio 0.8, 0.1 and 0.1.
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NOTE: Since there is no data attributes in some of these datasets, we use node_id (in one-hot vector whose length is the max number of nodes across all graphs) as the node feature. Also note that the node_id in some datasets is not unique (e.g. a graph may has two nodes with the same id).
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| | DD | PROTEINS | NCI1 | NCI109 | Mutagenicity | ENZYMES |
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| ---------------- | ------ | -------- | ----- | ------ | ------------ | ------- |
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| NumGraphs | 1178 | 1113 | 4110 | 4127 | 4337 | 600 |
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| AvgNodesPerGraph | 284.32 | 39.06 | 29.87 | 29.68 | 30.32 | 32.63 |
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| AvgEdgesPerGraph | 715.66 | 72.82 | 32.30 | 32.13 | 30.77 | 62.14 |
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| NumFeats | 89 | 1 | 37 | 38 | 14 | 18 |
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| NumClasses | 2 | 2 | 2 | 2 | 2 | 6 |
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How to run example files
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In the HGP-SL-DGL folder, run
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```bash
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python main.py --dataset ${your_dataset_name_here} [hyper-parameters]
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```
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If want to use a GPU, run
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```bash
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python main.py --device ${your_device_id_here} --dataset ${your_dataset_name_here} [hyper-parameters]
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```
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For example, to perform experiments on DD dataset on GPU, run:
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```bash
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python main.py --device 0 --dataset DD --lr 0.0001 --batch_size 64 --pool_ratio 0.3 --dropout 0.5 --conv_layers 2
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```
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NOTE: Be careful when modifying `batch_size` and `pool_ratio` for large dataset like DD. Too large batch size or pooling ratio may cause out-of-memory and other severe errors.
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You can find the detailed hyper-parameter settings below (in the Performance section).
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Performance
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-------------------------
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**Hyper-parameters**
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This part is directly from [author's implementation](https://github.com/cszhangzhen/HGP-SL)
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| Datasets | lr | weight_decay | batch_size | pool_ratio | dropout | net_layers |
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| ------------- | --------- | -------------- | --------------- | -------------- | -------- | ---------- |
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| PROTEINS | 0.001 | 0.001 | 512 | 0.5 | 0.0 | 3 |
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| Mutagenicity | 0.001 | 0.001 | 512 | 0.8 | 0.0 | 3 |
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| NCI109 | 0.001 | 0.001 | 512 | 0.8 | 0.0 | 3 |
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| NCI1 | 0.001 | 0.001 | 512 | 0.8 | 0.0 | 3 |
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| DD | 0.0001 | 0.001 | 64 | 0.3 | 0.5 | 2 |
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| ENZYMES | 0.001 | 0.001 | 128 | 0.8 | 0.0 | 2 |
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**Accuracy**
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**NOTE**: We find that there is a gap between accuracy obtained via author's code and the one reported in the [paper]((https://arxiv.org/pdf/1911.05954.pdf)). An issue has been proposed in the author's repo (see [here](https://github.com/cszhangzhen/HGP-SL/issues/8)).
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| | Mutagenicity | NCI109 | NCI1 | DD |
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| -------------------------- | ------------ | ----------- | ----------- | ----------- |
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| Reported in Paper | 82.15(0.58) | 80.67(1.16) | 78.45(0.77) | 80.96(1.26) |
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| Author's Code (full graph) | 78.44(2.10) | 74.44(2.05) | 77.37(2.09) | OOM |
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| Author's Code (sample) | 79.68(1.68) | 73.86(1.72) | 76.29(2.14) | 75.46(3.86) |
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| DGL (full graph) | 79.52(2.21) | 74.86(1.99) | 74.62(2.22) | OOM |
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| DGL (sample) | 79.15(1.62) | 75.39(1.86) | 73.77(2.04) | 76.47(2.14) |
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**Speed**
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Device: Tesla V100-SXM2 16GB
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In seconds
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| | DD(batchsize=64), large graph | Mutagenicity(batchsize=512), small graph |
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| ----------------------------- | ----------------------------- | ---------------------------------------- |
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| Author's code (sample) | 9.96 | 12.91 |
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| Author's code (full graph) | OOM | 13.03 |
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| DGL (sample) | 9.50 | 3.59 |
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| DGL (full graph) | OOM | 3.56 |
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