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
派生自 sadaszewski/decagon-pytorch
1
0
派生 0
代码 工单 0 合并请求 0 版本发布 0 百科 动态
您最多选择25个主题 主题必须以字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
169 提交
1 分支
866KB
目录树: 058d4d43fb
分支列表 标签列表
${ item.name }
创建分支 ${ searchTerm }
从 '058d4d43fb'
${ noResults }
Stanislaw Adaszewski 058d4d43fb Add _sparse_diag_cat(). 4 年前
docker Start working on experiments/decagon_run. 4 年前
docs Update matrix-multiply. 4 年前
experiments Add Citing note. 4 年前
src Add _sparse_diag_cat(). 4 年前
tests Add _sparse_diag_cat(). 4 年前
.empty Initial commit. 4 年前
.gitignore Add test_timing_05(). 4 年前
README.md Add Citing note. 4 年前
requirements.txt Start icosagon. 4 年前

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.