Implicit and Explicit Graph Embedding: Comparison of both Approaches on Chemoinformatics Applications

Benoit Gaüzère (1) , Hasegawa Makoto (2) , Luc Brun (1) , Salvatore Tabbone (2)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 QGAR - Querying Graphics through Analysis and Recognition

Defining similarities or distances between graphs is one of the bases of the structural pattern recognition field. An important trend within this field consists in going beyond the simple formulation of simi- larity measures by studying properties of graph's spaces induced by such distance or similarity measures . Such a problematic is closely related to the graph embedding problem. In this article, we investigate two types of similarity measures. The first one is based on the notion of graph edit distance which aims to catch a global dissimilarity between graphs. The second family is based on comparisons of bags of patterns extracted from graphs to be compared. Both approaches are detailed and their performances are evaluated on different chemoinformatics problems.

Mots clés: implicit embedding; Explicit

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

Benoit Gaüzère, Hasegawa Makoto, Luc Brun, Salvatore Tabbone. Implicit and Explicit Graph Embedding: Comparison of both Approaches on Chemoinformatics Applications. Joint IAPR International Workshop, SSPR & SPR 2012, Nov 2012, Hiroshima, Japan. pp.510-518, ⟨10.1007/978-3-642-34166-3_56⟩. ⟨hal-00768654⟩