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
изданак од sadaszewski/decagon-pytorch
1
0
Креирај огранак 0
Код Дискусије 0 Захтеви за спајање 0 Издања 0 Вики Activity
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.
132 Комити
1 Грана
866KB
Дрво: a6de8c7846
Гране Ознаке
${ item.name }
Create branch ${ searchTerm }
from 'a6de8c7846'
${ noResults }
Stanislaw Adaszewski a6de8c7846 Add test_flatten_predictions_05(). пре 4 година
docs Add icosagon-reltype-rules. пре 4 година
src Change test in test_unigram_03(). пре 4 година
tests Add test_flatten_predictions_05(). пре 4 година
.empty Initial commit. пре 4 година
.gitignore Add icosagon classes diagram. пре 4 година
README.md Update README.md пре 4 година
requirements.txt Start icosagon. пре 4 година

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.

References

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