Update README.md

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.