dgl/examples/pytorch/gxn

• DGL Implementation of Graph Cross Networks with Vertex Infomax Pooling (NeurIPS 2020)
• • The graph dataset used in this example
  • How to run example files
  • Performance

DGL Implementation of Graph Cross Networks with Vertex Infomax Pooling (NeurIPS 2020)

This DGL example implements the GNN model proposed in the paper Graph Cross Networks with Vertex Infomax Pooling.
The author's codes of implementation is in here

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', 'ENZYMES', 'IMDB-BINARY', 'IMDB-MULTI' and 'COLLAB' in this GXN implementation. All these datasets are randomly splited to train and test set with ratio 0.9 and 0.1 (which is similar to the setting in the author's implementation).

NOTE: Follow the setting of the author's implementation, for 'DD' and 'PROTEINS', we use one-hot node label as input node features. For ENZYMES', 'IMDB-BINARY', 'IMDB-MULTI' and 'COLLAB', we use the concatenation of one-hot node label (if available) and one-hot node degree as input node features.

	DD	PROTEINS	ENZYMES	IMDB-BINARY	IMDB-MULTI	COLLAB
NumGraphs	1178	1113	600	1000	1500	5000
AvgNodesPerGraph	284.32	39.06	32.63	19.77	13.00	74.49
AvgEdgesPerGraph	715.66	72.82	62.14	96.53	65.94	2457.78
NumFeats	89	1	18	-	-	-
NumClasses	2	2	6	2	3	2

How to run example files

If you want to reproduce the author's result, at the root directory of this example (gxn), run

bash scripts/run_gxn.sh ${dataset_name} ${device_id} ${num_trials} ${print_trainlog_every}

If you want to perform a early-stop version experiment, at the root directory of this example, run

bash scripts/run_gxn_early_stop.sh ${dataset_name} ${device_id} ${num_trials} ${print_trainlog_every}

where

• dataset_name: Dataset name used in this experiment. Could be DD', 'PROTEINS', 'ENZYMES', 'IMDB-BINARY', 'IMDB-MULTI' and 'COLLAB'.
• device_id: ID of computation device. -1 for pure CPU computation. For example if you only have single GPU, set this value to be 0.
• num_trials: How many times does the experiment conducted.
• print_training_log_every: Print training log every ? epochs. -1 for silent training.

NOTE: If your have problem when using 'IMDB-BINARY', 'IMDB-MULTI' and 'COLLAB', it could be caused by a bug in LegacyTUDataset/TUDataset in DGL (see here). If your DGL version is less than or equal to 0.5.3 and you encounter problems like "undefined variable" (LegacyTUDataset) or "the argument force_reload=False does not work" (TUDataset), try:

• use TUDataset with force_reload=True
• delete dataset files
• change degree_as_feature(dataset) and node_label_as_feature(dataset, mode=mode) to degree_as_feature(dataset, save=False) and node_label_as_feature(dataset, mode=mode, save=False) in main.py.

Performance

Accuracy

NOTE: Different from our implementation, the author uses fixed dataset split. Thus there may be difference between our result and the author's result. To compare our implementation with the author's, we follow the setting in the author's implementation that performs model-selection on testset. We also try early-stop with patience equals to 1/5 of the total number of epochs for some datasets. The result of Author's Code in the table below are obtained using first-ford data as the test dataset.

	DD	PROTEINS	ENZYMES	IMDB-BINARY	IMDB-MULTI	COLLAB
Reported in Paper	82.68(4.1 )	79.91(4.1)	57.50(6.1)	78.60(2.3)	55.20(2.5)	78.82(1.4)
Author's Code	82.05	72.07	58.33	77.00	56.00	80.40
DGL	82.97(3.0)	78.21(2.0)	57.50(5.5)	78.70(4.0)	52.26(2.0)	80.58(2.4)
DGL(early-stop)	78.66(4.3)	73.12(3.1)	39.83(7.4)	68.60(6.7)	45.40(9.4)	76.18(1.9)

Speed

Device:

• CPU: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
• GPU: Tesla V100-SXM2 16GB

In seconds

	DD	PROTEINS	ENZYMES	IMDB-BINARY	IMDB-MULTI	COLLAB(batch_size=64)	COLLAB(batch_size=20)
Author's Code	25.32	2.93	1.53	2.42	3.58	96.69	19.78
DGL	2.64	1.86	1.03	1.79	2.45	23.52	32.29