Ivan Olmos and Jesus A. Gonzalez, Instituto Nacional de Astrofísica, óptica, y Electrónica; and Mauricio Osorio, Universidad de las Américas Puebla
Subgraph Isomorphism Detection is an important problem for several computer science subfields, where a graph-based representation is used. In this research we present a new approach to find a Subgraph Isomorphism (SI) using a list code based representation without candidate generation. We implement a step by step expansion model with a width-depth search. Our experiments show a promising method to be used with scalable graph matching tools to find interesting patterns in Machine Learning and Data Mining applications.