dmlc--dgl
Chen Sirui
673cc64c8c
* Add hgat example * Add experiment * Clean code * clear the code * Add index in README * Add index in README * Add index in README * Add index in README * Add index in README * Add index in README * Change the code title and folder name * Ready to merge * Prepare for rebase and change message passing function * use git ignore to handle empty file * change file permission to resolve empty file * Change permission * change file mode Co-authored-by: Chen <chesirui@3c22fbe5458c.ant.amazon.com> Co-authored-by: Tianjun Xiao <xiaotj1990327@gmail.com>
1.9 KiB
1.9 KiB
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.