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Tong Zhao e9a05cafec | 2 years ago | |
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cora | 5 years ago | |
pubmed-data | 5 years ago | |
src | 4 years ago | |
.gitignore | 5 years ago | |
README.md | 5 years ago | |
example.sh | 5 years ago | |
requirements.txt | 2 years ago |
This package contains a PyTorch implementation of GraphSAGE.
Tianwen Jiang (tjiang2@nd.edu),
Tong Zhao (tzhao2@nd.edu),
Daheng Wang (dwang8@nd.edu).
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
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