# Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach

@inproceedings{Lopez2021SymmetricSF, title={Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach}, author={F. Javier L'opez and B{\'e}atrice Pozzetti and Steve J. Trettel and Michael Strube and Anna Wienhard}, booktitle={International Conference on Machine Learning}, year={2021} }

Learning faithful graph representations as sets of vertex embeddings has become a fundamental intermediary step in a wide range of machine learning applications. We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets. This enables us to introduce a new method, the use of Finsler metrics integrated in a Riemannian optimization scheme, that better adapts to dissimilar structures in the graph. We develop a…

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