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## GraphSAGE: Inductive Representation Learning on Large Graphs
## GraphSage: Inductive Representation Learning on Large Graphs
#### Authors: [William L. Hamilton](http://stanford.edu/~wleif) (wleif@stanford.edu), [Rex Ying](http://joy-of-thinking.weebly.com/) (rexying@stanford.edu)
#### [Project Website](http://snap.stanford.edu/graphsage/)
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### Overview
This directory contains code necessary to run the GraphSAGE algorithm.
This directory contains code necessary to run the GraphSage algorithm.
GraphSage can be viewed as a stochastic generalization of graph convolutions, and it is especially useful for massive, dynamic graphs that contain rich feature information.
See our [paper](https://arxiv.org/pdf/1706.02216.pdf) for details on the algorithm.
The example_data subdirectory contains a small example of the protein-protein interaction data,
which includes 3 training graphs + one validation graph and one test graph.
The full Reddit and PPI datasets (described in the paper) are available on the [project website](http://snap.stanford.edu/graphsage/).
If you make use of this code or the GraphSAGE algorithm in your work, please cite the following paper:
If you make use of this code or the GraphSage algorithm in your work, please cite the following paper:
@article{hamilton2017inductive,
author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure},
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#### Using the output of the unsupervised models
The unsupervised variants of GraphSAGE will output embeddings to the logging directory as described above.
The unsupervised variants of GraphSage will output embeddings to the logging directory as described above.
These embeddings can then be used in downstream machine learning applications.
The `eval_scripts` directory contains examples of feeding the embeddings into simple logistic classifiers.