53 lines
2.8 KiB
Markdown
53 lines
2.8 KiB
Markdown
## GraphSAGE: Inductive Representation Learning on Large Graphs
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#### Authors: [William Hamilton](http://stanford.edu/~wleif) (wleif@stanford.edu), [Rex Ying](http://joy-of-thinking.weebly.com/) (rexying@stanford.edu)
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#### [Project Website](http://snap.stanford.edu/graphsage/)
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### Overview
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This directory contains code necessary to run the GraphSAGE algorithm.
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See our paper for details on the algorithm: TODO arxiv link.
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The example_data subdirectory contains a small example of the PPI data,
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which includes 3 training networks + one validation network and one test network.
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The full Reddit and PPI datasets are available on the [project website](http://snap.stanford.edu/graphsage/).
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If you make use of this code in your work, please cite the following paper:
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### Requirements
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Recent versions of TensorFlow, numpy, scipy, and networkx are required.
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### Running the code
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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.
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Note that example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.
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#### Input format
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As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files:
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* <train_prefix>-G.json -- "A networkx-specified json file describing the input graph."
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* <train_prefix>-id_map.json -- "A json-stored dictionary mapping the graph node ids to consecutive integers."
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* <train_prefix>-id_map.json -- "A json-stored dictionary mapping the graph node ids to classes."
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* <train_prefix>-feats.npy --- "A numpy-stored array of node features; ordering given by id_map.json"
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* <train_prefix>-walks.txt --- "A text file specifying random walk co-occurrences (one pair per line)" (*only for unsupervised)
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#### Model variants
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The user must also specify a --model, the variants of which are described in detail in the paper:
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* graphsage_mean -- GraphSAGE with mean-based aggregator
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* graphsage_seq -- GraphSAGE with LSTM-based aggregator
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* graphsage_pool -- GraphSAGE with max-pooling aggregator
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* gcn -- GraphSAGE with GCN-based aggregator
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* n2v -- an implementation of [DeepWalk](https://arxiv.org/abs/1403.6652) (called n2v for short in the code.)
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#### Logging directory
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Finally, a --base_log_dir should be specified (it defaults to the current directory).
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The output of the model and log files will be stored in a subdirectory of the base_log_dir.
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The supervised model will output F1 scores, while the unsupervised model will train embeddings and store them.
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The unsupervised embeddings will be stored at val.npy with val.txt specifying the order of embeddings as a per-line list of node ids.
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Note that the full log outputs and stored embeddings can be 5-10Gb in size (on the full data).
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#### Using the output of the unsupervised models
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TODO
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