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README.md

HardGAT

DGL Implementation of h/cGAO paper.

This DGL example implements the GNN model proposed in the paper HardGraphAttention.

HardGANet implementor

This example was implemented by Ericcsr during his Internship work at the AWS Shanghai AI Lab.

The graph dataset used in this example

The DGL's built-in CoraGraphDataset. Dataset summary:

  • NumNodes: 2708
  • NumEdges: 10556
  • NumFeats: 1433
  • NumClasses: 7
  • NumTrainingSamples: 140
  • NumValidationSamples: 500
  • NumTestSamples: 1000

The DGL's build-in CiteseerGraphDataset. Dataset Summary:

  • NumNodes: 3327
  • NumEdges: 9228
  • NumFeats: 3703
  • NumClasses: 6
  • NumTrainingSamples: 120
  • NumValidationSamples: 500
  • NumTestSamples: 1000

The DGL's build-in PubmedGraphDataset. Dataset Summary:

  • NumNodes: 19717
  • NumEdges: 88651
  • NumFeats: 500
  • NumClasses: 3
  • NumTrainingSamples: 60
  • NumValidationSamples: 500
  • NumTestSamples: 1000

How to run example files

In the hgao folder, run

Please use train.py

python train.py --dataset=cora

If want to use a GPU, run

python train.py --gpu 0 --dataset=citeseer

If you want to use more Graph Hard Attention Modules

python train.py --num-layers <your number> --dataset=pubmed

If you want to change the hard attention threshold k

python train.py --k <your number> --dataset=cora

If you want to test with vanillia GAT

python train.py --model <gat/hgat> --dataset=cora

Performance

Models/Datasets Cora Citeseer Pubmed
GAT in DGL 81.5% 70.1% 77.7%
HardGAT 81.8% 70.2% 78.0%

Notice that HardGAT Simply replace GATConv with hGAO mentioned in paper.