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!
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.
Stanislaw Adaszewski a5b8701a0d Add some debug output for profiling, the bottleneck is in DecodeLayer but also comes generally from computing always all nodes. 3 years ago
docker Start working on experiments/decagon_run. 3 years ago
docs Add icosagon-reltype-rules. 3 years ago
experiments/decagon_run Shuffle. 3 years ago
src Add some debug output for profiling, the bottleneck is in DecodeLayer but also comes generally from computing always all nodes. 3 years ago
tests Add some debug output for profiling, the bottleneck is in DecodeLayer but also comes generally from computing always all nodes. 3 years ago
.empty Initial commit. 4 years ago
.gitignore Add icosagon classes diagram. 3 years ago
README.md Update README.md 4 years ago
requirements.txt Start icosagon. 3 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.