Graph abstraction for closed pattern mining in attributed networks

Abstract

We address the problem of finding patterns in an attributed graph. Our approach consists in extending the standard methodology of frequent closed pattern mining to the case in which the set of objects, in which are found the pattern supports, is the set of vertices of a graph, typically representing a social network. The core idea is then to define graph abstractions as subsets of the vertices satisfying some connectivity property within the corresponding induced subgraphs. Preliminary experiments illustrate the reduction in closed patterns we obtain as well as what kind of abstract knowledge is found via abstract implications rules.