diff --git a/README.md b/README.md index 6133a92..c6ee459 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,24 @@ -# 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. See our paper for details on the algorithm: TODO arxiv link. The example_data subdirectory contains a small example of the PPI data, which includes 3 training networks + one validation network and one test network. -The full Reddit and PPI datasets are available at: TODO -The Web of Science data can be released to groups or individuals with valid WoS access licenses. +The full Reddit and PPI datasets are available on the [project website](http://snap.stanford.edu/graphsage/). -## 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. -## Running the code +### Running 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.) @@ -30,7 +35,7 @@ The user must also specify a --model, the variants of which are described in det * graphsage_seq -- GraphSAGE with LSTM-based aggregator * graphsage_pool -- GraphSAGE with max-pooling 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). The output of the model and log files will be stored in a subdirectory of the base_log_dir.