Geometry of Graph Embeddings

We describe work on applying the geometry of symmetric spaces to graph embedding problems in machine learning. Using the rich structure of the space of symmetric positive definite matrices, we develop tools from Riemannian geometry to more faithfully represent complex graph data. Joint work with Fede Lopez, Anna Wienhard, Bea Pozzetti and Michel Strube at the University of Heidelberg.