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
atdalīts no sadaszewski/decagon-pytorch
1
0
Atdalīts 0
Kods Problēmas 0 Izmaiņu pieprasījumi 0 Laidieni 0 Vikivietne Aktivitāte
Nevar pievienot vairāk kā 25 tēmas Tēmai ir jāsākas ar burtu vai ciparu, tā var saturēt domu zīmes ('-') un var būt līdz 35 simboliem gara.
169 Revīzijas
1 Atzars
866KB
Koks: 058d4d43fb
Atzari Tagi
${ item.name }
Izveidot atzaru ${ searchTerm }
no '058d4d43fb'
${ noResults }
Stanislaw Adaszewski 058d4d43fb Add _sparse_diag_cat(). pirms 4 gadiem
docker Start working on experiments/decagon_run. pirms 4 gadiem
docs Update matrix-multiply. pirms 4 gadiem
experiments Add Citing note. pirms 4 gadiem
src Add _sparse_diag_cat(). pirms 4 gadiem
tests Add _sparse_diag_cat(). pirms 4 gadiem
.empty Initial commit. pirms 4 gadiem
.gitignore Add test_timing_05(). pirms 4 gadiem
README.md Add Citing note. pirms 4 gadiem
requirements.txt Start icosagon. pirms 4 gadiem

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.