Graph embedding through probabilistic graphical model applied to symbolic graphs

Hana Jarraya*, Oriol Ramos Terrades, Josep Lladós

*Corresponding author for this work

Research output: Book/ReportProceedingResearchpeer-review


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.

Original languageEnglish
Number of pages8
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10255 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Attributed graph
  • Graph embedding
  • Probabilistic graphical model
  • Structured support vector machines


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