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!
sadaszewski1
/
decagon-pytorch
forkad från sadaszewski/decagon-pytorch
1
0
Förgrening 0
Kod Ärenden 0 Pull-förfrågningar 0 Släpp 0 Wiki Aktiviteter
Du kan inte välja fler än 25 ämnen Ämnen måste starta med en bokstav eller siffra, kan innehålla bindestreck ('-') och vara max 35 tecken långa.
187 Incheckningar
1 Gren
866KB
Träd: 82d5c06eee
Grenar Taggar
${ item.name }
Skapa branchen ${ searchTerm }
från '82d5c06eee'
${ noResults }
Stanislaw Adaszewski 82d5c06eee Fix aggregation of representations from different edge types. 4 år sedan
docker Start working on experiments/decagon_run. 4 år sedan
docs Work on required vertices per layer. 4 år sedan
experiments Add Citing note. 4 år sedan
src Fix aggregation of representations from different edge types. 4 år sedan
tests Make work convolve() in new model. 4 år sedan
.empty Initial commit. 4 år sedan
.gitignore Add test_timing_05(). 4 år sedan
README.md Add Citing note. 4 år sedan
requirements.txt Start icosagon. 4 år sedan

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.