A Method for Classifying Vertices of Labeled Graphs Applied to Knowledge Discovery from Molecules

Autor: Pennerath, Frédéric, Polaillon, Géraldine, Napoli, Amedeo
Přispěvatelé: SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE), SUPELEC-Campus Gif, Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2008
Předmět:
Zdroj: Proceedings of the 18th European Conference on Artificial Intelligence
ECAI'2008
ECAI'2008, Jul 2008, Patras, Greece. pp.147-151, ⟨10.3233/978-1-58603-891-5-147⟩
DOI: 10.3233/978-1-58603-891-5-147⟩
Popis: International audience; The article proposes a generic method to classify vertices or edges of a labeled graph. More precisely the method computes a confidence index for each vertex v or edge e to be a member of a target class by mining the topological environments of v or e. The method contributes to knowledge discovery since it exhibits for each edge or vertex an informative environnement that explains the found confidence. When applied to the problem of discovering strategic bonds in molecules, the method correctly classifies most of the bonds while providing relevant explanations to chemists. The developed algorithm GemsBond outperforms both speed and scalability of the learning method that has previously been applied to the same application while giving similar results.
Databáze: OpenAIRE
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