| @@ -1,6 +1,6 @@ | |||||
| = decagon-pytorch | |||||
| # decagon-pytorch | |||||
| == Introduction | |||||
| ## Introduction | |||||
| [Decagon](https://github.com/mims-harvard/decagon) | [Decagon](https://github.com/mims-harvard/decagon) | ||||
| is a method for learning node embeddings in multimodal graphs, | is a method for learning node embeddings in multimodal graphs, | ||||
| @@ -9,7 +9,7 @@ settings. | |||||
| Decagon-PyTorch is a PyTorch reimplementation of the algorithm. | Decagon-PyTorch is a PyTorch reimplementation of the algorithm. | ||||
| == References | |||||
| ## References | |||||
| 1. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). | 1. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). | ||||
| [Modeling polypharmacy side effects with graph convolutional networks](https://academic.oup.com/bioinformatics/article/34/13/i457/5045770) | [Modeling polypharmacy side effects with graph convolutional networks](https://academic.oup.com/bioinformatics/article/34/13/i457/5045770) | ||||