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| # decagon-pytorch | |||||
| ## Introduction | |||||
| [Decagon](https://github.com/mims-harvard/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](https://academic.oup.com/bioinformatics/article/34/13/i457/5045770) | |||||
| Bioinformatics, 34(13), i457-i466. | |||||