@Article{JaumeGibert2012,
author="Jaume Gibert
and Ernest Valveny
and Horst Bunke",
title="Graph Embedding in Vector Spaces by Node Attribute Statistics",
journal="Pattern Recognition",
year="2012",
volume="45",
number="9",
pages="3072--3083",
optkeywords="Structural pattern recognition",
optkeywords="Graph embedding",
optkeywords="Data clustering",
optkeywords="Graph classification",
abstract="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. Graphembedding into vectorspaces 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 nodeattributes 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.",
optnote="DAG",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1992), last updated on Thu, 13 Mar 2014 15:51:42 +0100",
issn="0031-3203",
doi="10.1016/j.patcog.2012.01.009",
file=":http://refbase.cvc.uab.es/files/GVB2012a.pdf:PDF"
}