# DGL Implementation of the HGP-SL Paper

This DGL example implements the GNN model proposed in the paper [Hierarchical Graph Pooling with Structure Learning](https://arxiv.org/pdf/1911.05954.pdf). 
The author's codes of implementation is in [here](https://github.com/cszhangzhen/HGP-SL)


Example implementor
----------------------
This example was implemented by [Tianqi Zhang](https://github.com/lygztq) during his Applied Scientist Intern work at the AWS Shanghai AI Lab.


The graph dataset used in this example 
---------------------------------------
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.

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       |


How to run example files
--------------------------------
In the HGP-SL-DGL folder, run

```bash
python main.py --dataset ${your_dataset_name_here} [hyper-parameters]
```

If want to use a GPU, run

```bash
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:

```bash
python main.py --device 0 --dataset DD --lr 0.0001 --batch_size 64 --pool_ratio 0.3 --dropout 0.5 --conv_layers 2
```

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.

You can find the detailed hyper-parameter settings below (in the Performance section).

Performance
-------------------------

**Hyper-parameters**

This part is directly from [author's implementation](https://github.com/cszhangzhen/HGP-SL)

| 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]((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)).

|                            | 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                                     |