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Added in acknowledgments.
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@ -70,3 +70,9 @@ 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.
#### Acknowledgements
The original version of this code base was originally forked from https://github.com/tkipf/gcn/, and we owe many thanks to Thomas Kipf for making his code available.
We also thank Yuanfang Li and Xin Li who contributed to a course project that was based on this work.
Please see the [paper](https://arxiv.org/pdf/1706.02216.pdf) for funding details and additional (non-code related) acknowledgements.