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William L Hamilton 2017-05-31 14:51:58 +01:00 committed by GitHub
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@ -47,9 +47,10 @@ The user must also specify a --model, the variants of which are described in det
#### Logging directory #### Logging directory
Finally, a --base_log_dir should be specified (it defaults to the current directory). 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. The output of the model and log files will be stored in a subdirectory of the base_log_dir.
The path to the logged data will be of the form `<sup/unsup>-<data_prefix>/graphsage-<model_description>/`.
The supervised model will output F1 scores, while the unsupervised model will train embeddings and store them. The supervised model will output F1 scores, while the unsupervised model will train embeddings and store them.
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. The unsupervised embeddings will be stored in a numpy formated file named val.npy with val.txt specifying the order of embeddings as a per-line list of node ids.
Note that the full log outputs and stored embeddings can be 5-10Gb in size (on the full data). Note that the full log outputs and stored embeddings can be 5-10Gb in size (on the full data when running with the unsupervised variant).
#### Using the output of the unsupervised models #### Using the output of the unsupervised models