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## GraphSAGE: Inductive Representation Learning on Large Graphs
#### Authors: [William Hamilton](http://stanford.edu/~wleif) (wleif@stanford.edu), [Rex Ying](http://joy-of-thinking.weebly.com/) (rexying@stanford.edu)
#### 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/)
### Overview
This directory contains code necessary to run the GraphSAGE algorithm.
See our paper for details on the algorithm: TODO arxiv link.
See our [paper](http://TODO) for details on the algorithm.
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 on the [project website](http://snap.stanford.edu/graphsage/).
If you make use of this code 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:
### Requirements
@ -22,15 +22,16 @@ Recent versions of TensorFlow, numpy, scipy, and networkx are required.
The example_unsupervised.sh and example_supervised.sh files contain example usages of the code, which use the unsupervised and supervised variants of GraphSAGE, respectively.
Note that example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.
We generally found that performance continued to improve even after the loss was very near convergence (i.e., even when the loss was decreasing at a very slow rate).
#### Input format
As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files:
* <train_prefix>-G.json -- "A networkx-specified json file describing the input graph."
* <train_prefix>-id_map.json -- "A json-stored dictionary mapping the graph node ids to consecutive integers."
* <train_prefix>-id_map.json -- "A json-stored dictionary mapping the graph node ids to classes."
* <train_prefix>-feats.npy --- "A numpy-stored array of node features; ordering given by id_map.json"
* <train_prefix>-walks.txt --- "A text file specifying random walk co-occurrences (one pair per line)" (*only for unsupervised)
* <train_prefix>-G.json -- A networkx-specified json file describing the input graph. Nodes have 'val' and 'test' attributes specifying if they are a part of the validation and test sets, respectively.
* <train_prefix>-id_map.json -- A json-stored dictionary mapping the graph node ids to consecutive integers.
* <train_prefix>-id_map.json -- A json-stored dictionary mapping the graph node ids to classes.
* <train_prefix>-feats.npy --- A numpy-stored array of node features; ordering given by id_map.json
* <train_prefix>-walks.txt --- A text file specifying random walk co-occurrences (one pair per line) (*only for unsupervised version of graphsage)
To run the model on a new dataset, you need to make data files in the format described above.
To run random walks for the unsupervised model and to generate the <prefix>-walks.txt file)