Reducing the dimensionality of vector space embeddings of graphs

Graphs are a convenient representation formalism for structured objects, but they suffer from the fact that only a few algorithms for graph classification and clustering exist. In this paper we propose a new approach to graph classification by embedding graphs in real vector spaces. This approach allows us to apply advanced classification tools while retaining the high representational power of graphs. The basic idea of our approach is to regard the edit distances of a given graph g to a set of training graphs as a vectorial description of g. Once a graph has been transformed into a vector, different dimensionality reduction algorithms are applied such that redundancies are eliminated. To this reduced vectorial data representation, pattern classification algorithms can be applied. Through various experimental results we show that the proposed vector space embedding and subsequent classification with the reduced vectors outperform the classification algorithms in the original graph domain.