This repository includes the following three python scripts:
run.py
: AFFN test code for the following datasets: cora, citeseer, pubmed, amazon co-purchase, coauthor.run_arxiv.py
: AFFN test code for the ogbn-arxiv dataset.logger.py
: Utils for logging outputs.
-
Python >= 3.5.0
-
Pyorch >= 1.5.0
-
DGL >= 0.4.0. To install DGL, run
pip install dgl
. -
For CUDA builds, require CUDA >= 9.0. To install DGL with CUDA, run
pip install dgl-${CUDA}
, replace${CUDA}
with your CUDA version, i.e.cu90
,cu92
,cu100
orcu101
# Run with default config
python run.py
python run_arxiv.py
# Run with custom config
python run.py --runs=5 --epochs=200 --hidden_channels=256 --dataset=amazon-computers --model=AFFN
python run_arxiv.py --hidden_channels=256 --dropout=0.65 --lr=1e-3 --wd=5e-4 --model=GCN
args | type | meaning | default |
---|---|---|---|
--device |
int |
GPU device number | 0 |
--hidden_channels |
int |
Number of nodes in hidden layers | 256 |
--dropout |
float |
Dropout rate in dropout layer | 0.5 |
--lr |
float |
Learning rate | 0.01 |
--wd |
float |
L2 regularization coefficient | 0 |
--epochs |
int |
Number of epochs in each run | 500 |
--runs |
int |
Number of runs with current parameters | 10 |
--dataset * |
string: {cora, pubmed, citeseer, amazon-computers, amazon-photo, coauthor-cs, coauthor-physics} |
Which dataset to use | cora |
--model |
string: {AFFN, GCN, SAGE, GAT} |
Which model to use | AFFN |
*Script run_arxiv.py
has NO arg --dataset
.
Note: when running for the first time, it will take sometime to download the corresponding dataset automatically.