This DGL example implements the GNN model proposed in the paper Hierarchical Graph Pooling with Structure Learning. The author's codes of implementation is in here
This example was implemented by Tianqi Zhang during his Applied Scientist Intern work at the AWS Shanghai AI Lab.
The DGL's built-in 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.
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).
| DD | PROTEINS | NCI1 | NCI109 | Mutagenicity | ENZYMES | |
|---|---|---|---|---|---|---|
| NumGraphs | 1178 | 1113 | 4110 | 4127 | 4337 | 600 |
| AvgNodesPerGraph | 284.32 | 39.06 | 29.87 | 29.68 | 30.32 | 32.63 |
| AvgEdgesPerGraph | 715.66 | 72.82 | 32.30 | 32.13 | 30.77 | 62.14 |
| NumFeats | 89 | 1 | 37 | 38 | 14 | 18 |
| NumClasses | 2 | 2 | 2 | 2 | 2 | 6 |
In the HGP-SL-DGL folder, run
python main.py --dataset ${your_dataset_name_here} [hyper-parameters]If want to use a GPU, run
python main.py --device ${your_device_id_here} --dataset ${your_dataset_name_here} [hyper-parameters]For example, to perform experiments on DD dataset on GPU, run:
python main.py --device 0 --dataset DD --lr 0.0001 --batch_size 64 --pool_ratio 0.3 --dropout 0.5 --conv_layers 2NOTE: 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.
You can find the detailed hyper-parameter settings below (in the Performance section).
Hyper-parameters
This part is directly from author's implementation
| Datasets | lr | weight_decay | batch_size | pool_ratio | dropout | net_layers |
|---|---|---|---|---|---|---|
| PROTEINS | 0.001 | 0.001 | 512 | 0.5 | 0.0 | 3 |
| Mutagenicity | 0.001 | 0.001 | 512 | 0.8 | 0.0 | 3 |
| NCI109 | 0.001 | 0.001 | 512 | 0.8 | 0.0 | 3 |
| NCI1 | 0.001 | 0.001 | 512 | 0.8 | 0.0 | 3 |
| DD | 0.0001 | 0.001 | 64 | 0.3 | 0.5 | 2 |
| ENZYMES | 0.001 | 0.001 | 128 | 0.8 | 0.0 | 2 |
Accuracy
NOTE: We find that there is a gap between accuracy obtained via author's code and the one reported in the paper. An issue has been proposed in the author's repo (see here).
| Mutagenicity | NCI109 | NCI1 | DD | |
|---|---|---|---|---|
| Reported in Paper | 82.15(0.58) | 80.67(1.16) | 78.45(0.77) | 80.96(1.26) |
| Author's Code (full graph) | 78.44(2.10) | 74.44(2.05) | 77.37(2.09) | OOM |
| Author's Code (sample) | 79.68(1.68) | 73.86(1.72) | 76.29(2.14) | 75.46(3.86) |
| DGL (full graph) | 79.52(2.21) | 74.86(1.99) | 74.62(2.22) | OOM |
| DGL (sample) | 79.15(1.62) | 75.39(1.86) | 73.77(2.04) | 76.47(2.14) |
Speed
Device: Tesla V100-SXM2 16GB
In seconds
| DD(batchsize=64), large graph | Mutagenicity(batchsize=512), small graph | |
|---|---|---|
| Author's code (sample) | 9.96 | 12.91 |
| Author's code (full graph) | OOM | 13.03 |
| DGL (sample) | 9.50 | 3.59 |
| DGL (full graph) | OOM | 3.56 |