Graph embedding in vector spaces by node attribute statistics

Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graph embedding into vector spaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous node attributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs.

► We tackle the problem of graph classification by embedding a set of graphs into a vector space.
► Feature vectors for graphs are constructed out of statistics on node labelling information.
► The methodology is applicable to continuous attributed graphs via a set of label representatives.
► A broad experimentation using several datasets of graphs is provided.
► Results show competitive accuracy rates with respect to other state of the art methodologies.