This is the implementation of paper:
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
The code is implemented in Python 3.7. Package used for development are just below.
networkx
numpy
scipy
torch
Pubmed
###Instructions for running the code
1, Run the subgraph sampling code
python3 subgraph_sample_pubmed.py
, the results will be stored in ./sampled_subgraph/
.
2, Run the GCN or GAT model training/testing code
python3 train_rw_gcn_pubmed.py
or
python3 train_rw_gat_pubmed.py
, the results will be shown on screen and stored in ./results/
.
###Note:
1, If no GPU is available, add config --no-cuda True
when running the GCN/GAT models.
2, To change epoch numbers (default as 10) of training to NUMBER, add config --epochs NUMBER
.