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
forked from sadaszewski/decagon-pytorch
1
0
Fork 0
Code Issues 0 Pull Requests 0 Releases 0 Wiki 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.
50 Commits
1 Branch
866KB
Tree: c1689b4985
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'c1689b4985'
${ noResults }
Stanislaw Adaszewski c1689b4985 Split decoders into cartesian and pairwise. 4 years ago
docs Better way to compute DecagonLayer. 4 years ago
src/decagon_pytorch Split decoders into cartesian and pairwise. 4 years ago
tests/decagon_pytorch Split decoders into cartesian and pairwise. 4 years ago
.empty Initial commit. 4 years ago
.gitignore Started implementing convolutions, with tests. 4 years ago
README.md Update README.md 4 years ago

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.