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Stanislaw Adaszewski 69e8ce5081 Add universal convolution units that can handle both sparse and dense inputs. 4 years ago
docs Better way to compute DecagonLayer. 4 years ago
src/decagon_pytorch Add universal convolution units that can handle both sparse and dense inputs. 4 years ago
tests/decagon_pytorch Add universal convolution units that can handle both sparse and dense inputs. 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.