A PyTorch implementation of GraphSAGE
This package contains a PyTorch implementation of GraphSAGE.
Authors of this code package:
Tianwen Jiang (tjiang2@nd.edu), Tong Zhao (tzhao2@nd.edu), Daheng Wang (dwang8@nd.edu).
Environment settings
- python==3.6.8
- pytorch==1.0.0
Basic Usage
Main Parameters:
--dataSet The input graph dataset. (default: cora)
--agg_func The aggregate function. (default: Mean aggregater)
--epochs Number of epochs. (default: 50)
--b_sz Batch size. (default: 20)
--seed Random seed. (default: 824)
--unsup_loss The loss function for unsupervised learning. ('margin' or 'normal', default: normal)
--config Config file. (default: ./src/experiments.conf)
--cuda Use GPU if declared.
Learning Method
The user can specify a learning method by --learn_method, 'sup' is for supervised learning, 'unsup' is for unsupervised learning, and 'plus_unsup' is for jointly learning the loss of supervised and unsupervised method.
Example Usage
To run the unsupervised model on Cuda:
python -m src.main --epochs 50 --cuda --learn_method unsup