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# GraphSAGE code ## GraphSAGE: Inductive Representation Learning on Large Graphs
## Overview #### Authors: [William 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/)
### Overview
This directory contains code necessary to run the GraphSAGE algorithm. This directory contains code necessary to run the GraphSAGE algorithm.
See our paper for details on the algorithm: TODO arxiv link. See our paper for details on the algorithm: TODO arxiv link.
The example_data subdirectory contains a small example of the PPI data, The example_data subdirectory contains a small example of the PPI data,
which includes 3 training networks + one validation network and one test network. which includes 3 training networks + one validation network and one test network.
The full Reddit and PPI datasets are available at: TODO The full Reddit and PPI datasets are available on the [project website](http://snap.stanford.edu/graphsage/).
The Web of Science data can be released to groups or individuals with valid WoS access licenses.
## Requirements If you make use of this code in your work, please cite the following paper:
### Requirements
Recent versions of TensorFlow, numpy, scipy, and networkx are required. Recent versions of TensorFlow, numpy, scipy, and networkx are required.
## Running the code ### Running the code
The example_unsupervised.sh and example_supervised.sh files contain example usages of the code. The example_unsupervised.sh and example_supervised.sh files contain example usages of the code.
(example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.) (example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.)
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* graphsage_seq -- GraphSAGE with LSTM-based aggregator * graphsage_seq -- GraphSAGE with LSTM-based aggregator
* graphsage_pool -- GraphSAGE with max-pooling aggregator * graphsage_pool -- GraphSAGE with max-pooling aggregator
* gcn -- GraphSAGE with GCN-based aggregator * gcn -- GraphSAGE with GCN-based aggregator
* n2v -- an implementation of DeepWalk (called n2v for short everywhere) * n2v -- an implementation of [DeepWalk](https://arxiv.org/abs/1403.6652) (called n2v for short in the code.)
Finally, a --base_log_dir should be specified (it defaults to the current directory). Finally, a --base_log_dir should be specified (it defaults to the current directory).
The output of the model and log files will be stored in a subdirectory of the base_log_dir. The output of the model and log files will be stored in a subdirectory of the base_log_dir.