/examples/pytorch/ogb/ogbn-proteins/README.md

DGL for ogbn-proteins

GAT

DGL implementation of GAT for ogbn-proteins. Using some of the techniques from Bag of Tricks for Node Classification with Graph Neural Networks (https://arxiv.org/abs/2103.13355).

Requires DGL 0.5 or later versions.

Usage

For the best score, run gat.py and you should directly see the result.

python3 gat.py

For the score of GAT+labels, run gat.py with --use-labels enabled and you should directly see the result.

python3 gat.py --use-labels

Results

Here are the results over 10 runs.

Method	Validation ROC-AUC	Test ROC-AUC	#Parameters
GAT	0.9276 ± 0.0007	0.8747 ± 0.0016	2,475,232
GAT+labels	0.9280 ± 0.0008	0.8765 ± 0.0008	2,484,192

MWE-GCN and MWE-DGCN

Models

MWE-GCN and MWE-DGCN are GCN models designed for graphs whose edges contain multi-dimensional edge weights that indicate the strengths of the relations represented by the edges.

Dependencies

• DGL 0.5.2
• PyTorch 1.4.0
• OGB 1.2.0
• Tensorboard 2.1.1

Usage

To use MWE-GCN:

python main_proteins_full_dgl.py --model MWE-GCN

To use MWE-DGCN:

python main_proteins_full_dgl.py --model MWE-DGCN

Additional optional arguments include 'rand_seed' (the random seed), 'cuda' (the cuda device number, if available), 'postfix' (a string appended to the saved-model file)