dgl/examples/pytorch/hgp_sl

• DGL Implementation of the HGP-SL Paper
• • Example implementor
  • The graph dataset used in this example
  • How to run example files
  • Performance

DGL Implementation of the HGP-SL Paper

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

Example implementor

This example was implemented by Tianqi Zhang 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. 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

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 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

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