Graph clustering via inexact patterns

Abstract

Graph pattern mining is an important task in Data Mining and several algorithms have been proposed to solve this problem. Most of them require that a pattern and its occurrences are identical, thus, they rely on solving the graph isomorphism problem. In the last years, however, some algorithms have focused in the case in which label and edge structure differences between a pattern and its occurrences are allowed but maintaining a bijection among vertices, using inexact matching during the mining process. Recently, an algorithm that allows structural differences in vertices was proposed. This feature allows it to find patterns missed by other algorithms, but, do these extra patterns actually contain useful information? We explore the answer to this question by performing an experiment in the context of unsupervised mining tasks. Our results suggests that by allowing structural differences in both, vertices and edges, it is possible to obtain new useful information.