IF YOU WOULD LIKE TO GET AN ACCOUNT, please write an email to s dot adaszewski at gmail dot com. User accounts are meant only to report issues and/or generate pull requests. This is a purpose-specific Git hosting for ADARED projects. Thank you for your understanding!
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Stanislaw Adaszewski f99f0bb919 Add FastModel. 4 anni fa
docker Start working on experiments/decagon_run. 4 anni fa
docs Update matrix-multiply. 4 anni fa
experiments Add Citing note. 4 anni fa
src Add FastModel. 4 anni fa
tests Add test_fast_conv_layer_01() and test_fast_conv_layer_02(). 4 anni fa
.empty Initial commit. 4 anni fa
.gitignore Add test_timing_05(). 4 anni fa
README.md Add Citing note. 4 anni fa
requirements.txt Start icosagon. 4 anni fa

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

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