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Stanislaw Adaszewski fc8f9726af Remove unnecessary .detach() call in dropout_dense().		4年前
docker	Start working on experiments/decagon_run.	4年前
docs	Performance tests.	4年前
experiments	Add Citing note.	4年前
src	Remove unnecessary .detach() call in dropout_dense().	4年前
tests	Remove unnecessary .detach() call in dropout_dense().	4年前
.empty	Initial commit.	4年前
.gitignore	Add test_timing_05().	4年前
README.md	Add Citing note.	4年前
requirements.txt	Start icosagon.	4年前

README.md

decagon-pytorch

Introduction

Decagon is a method for learning node embeddings in multimodal graphs, and is especially useful for link prediction in highly multi-relational settings.

Decagon-PyTorch is a PyTorch reimplementation of the algorithm.

Citing

If you use this code in your research please cite this repository as:

Adaszewski S. (2020) https://code.adared.ch/sadaszewski/decagon-pytorch

References

Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks Bioinformatics, 34(13), i457-i466.