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@ -52,3 +52,9 @@ Note that the full log outputs and stored embeddings can be 5-10Gb in size (on t
The unsupervised variants of GraphSAGE will output embeddings to the logging directory as described above. The unsupervised variants of GraphSAGE will output embeddings to the logging directory as described above.
These embeddings can then be used in downstream machine learning applications. These embeddings can then be used in downstream machine learning applications.
The `eval_scripts` directory contains examples of feeding the embeddings into simple logistic classifiers. The `eval_scripts` directory contains examples of feeding the embeddings into simple logistic classifiers.
#### Running on a new dataset
To run the model on a new dataset, you need to make data files of the format described above.
To run random walks for the unsupervised model (and to generate the <prefix>-walks.txt file)
you can use the `run_walks` function in `graphsage.utils`.