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DGL Implementation of the SAGPool Paper

This DGL example implements the GNN model proposed in the paper Self Attention Graph 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', 'NCI1', 'NCI109' and 'Mutagenicity' in this SAGPool 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
NumGraphs 1178 1113 4110 4127 4337
AvgNodesPerGraph 284.32 39.06 29.87 29.68 30.32
AvgEdgesPerGraph 715.66 72.82 32.30 32.13 30.77
NumFeats 89 1 37 38 14
NumClasses 2 2 2 2 2

How to run example files

The valid dataset names (you can find a full list here):

  • 'DD' for D&D
  • 'PROTEINS' for PROTEINS
  • 'NCI1' for NCI1
  • 'NCI109' for NCI109
  • 'Mutagenicity' for Mutagenicity

In the sagpool folder, run

python main.py --dataset ${your_dataset_name_here}

If want to use a GPU, run

python main.py --device ${your_device_id_here} --dataset ${your_dataset_name_here}

If your want to perform a grid search, modify parameter settings in grid_search_config.json and run

python grid_search.py --device ${your_device_id_here} --num_trials ${num_of_trials_here}

Performance

NOTE: We do not perform grid search or finetune here, so there may be a gap between results in paper and our results. Also, we only perform 10 trials for each experiment, which is different from 200 trials per experiment in the paper.

The global architecture result

Dataset paper result (global) ours (global)
D&D 76.19 (0.94) 74.79 (2.69)
PROTEINS 70.04 (1.47) 70.36 (5.90)
NCI1 74.18 (1.20) 72.82 (2.36)
NCI109 74.06 (0.78) 71.64 (2.65)
Mutagenicity N/A 76.55 (2.89)

The hierarchical architecture result

Dataset paper result (hierarchical) ours (hierarchical)
D&D 76.45 (0.97) 75.38 (4.17)
PROTEINS 71.86 (0.97) 70.36 (5.68)
NCI1 67.45 (1.11) 70.61 (2.25)
NCI109 67.86 (1.41) 69.13 (3.85)
Mutagenicity N/A 75.20 (1.95)