42 lines
2.4 KiB
Markdown
42 lines
2.4 KiB
Markdown
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# GraphSAGE code
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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 at: TODO
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The Web of Science data can be released to groups or individuals with valid WoS access licenses.
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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.
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(example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.)
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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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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 (called n2v for short everywhere)
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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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The other inputs and hyperparameters are described in the TensorFlow flags.
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