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Basic reference pytorch implementation of GraphSAGE.
Forgive the messiness; it was coded in a rush.
# Reference PyTorch GraphSAGE Implementation
### Author: William L. Hamilton
Basic reference PyTorch implementation of GraphSAGE.
This reference implementation is not as fast as the TensorFlow version for large graphs, but the code is easier to read and it performs better on small-graph benchmarks.
The code is also intended to be more extensible and easier to work with the the TensorFlow version.
Currently, only supervised versions of GraphSAGE-mean and GraphSAGE-GCN are implemented.
#### Requirements
pytorch >0.2 is required.
#### Running examples
Execute `python -m graphsage.model` to run the Cora example.
It assumes that CUDA is not being used, but modifying the run functions in `model.py` in the obvious way can change this.