This is an anonymous implementation of DRGAT.

Step 1: Git clone this repo.

git clone https://github.com/anonymousaabc/DRGCN.git

Step 2: Install DGL environment.

cd DRGCN

sh drgat_env_install.sh

Step 3: Experimental datasets

Two datasets are required to run this code. We have already upload datasets in the directory. You can skip this step.

One is ogbn-arxiv origin data, the directory is ./drgat/dataset/ogbn_arxiv/.

The other is ogbn-arxiv pretrained node features from GIANT-XRT, the directory is ./drgat/dataset/ogbn-arxiv-pretrain/.

Step 4: Run the experiment.

GIANT+XRT+DRGAT: Run runexp_drgat_ogbnarxiv.sh for reproducing our results for ogbn-arxiv dataset with GIANT-XRT features.

cd drgat

sh runexp_drgat_ogbnarxiv.sh

GIANT+XRT+DRGAT+KD: Run runexp_drgat_ogbnarxiv_kd.sh for reproducing our results for ogbn-arxiv dataset with GIANT-XRT features and KD.

cd drgat

sh runexp_drgat_ogbnarxiv_kd.sh

Results

If execute correctly, you should have the following performance (using pretrained GIANT-XRT features).

Metrics	GIANT-XRT+DRGAT	GIANT-XRT+DRGAT+KD
Average val accuracy (%)	77.16 ± 0.08	77.25 ± 0.06
Average test accuracy (%)	76.11 ± 0.09	76.33 ± 0.08

Number of params: 2685527

Our hardware used for the experiments is Tesla P100-PCIE-16GB.

Remark: We do not fine-tune DRGAT for our GIANT-XRT. It is possible to achieve higher performance by fine-tune it more carefully.