PT Unknown AU Hana Jarraya Oriol Ramos Terrades Josep Llados TI Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs BT 8th Iberian Conference on Pattern Recognition and Image Analysis PY 2017 DE Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines AB We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. ER