Abstract

This paper introduces a novel approach for processing a general class
of structured information, viz., a graph of graphs structure, in which each node of
the graph can be described by another graph, and each node in this graph, in turn,
can be described by yet another graph, up to a finite depth. This graph of graphs
description may be used as an underlying model to describe a number of naturally
and artificially occurring systems, e.g. nested hypertexted documents. The
approach taken is a data driven method in that it learns from a set of examples how
to classify the nodes in a graph of graphs. To the best of our knowledge, this is
the first time that a machine learning approach is enabled to deal with such structured
problem domains. Experimental results on a relatively large scale real world
problem indicate that the learning is efficient. This paper presents some preliminary
results which show that the classification performance is already close to
those provided by the stateoftheart ones.