PT Journal
AU Jaume Gibert
   Ernest Valveny
   Horst Bunke
TI Graph Embedding in Vector Spaces by Node Attribute Statistics
SO Pattern Recognition
JI PR
PY 2012
BP 3072
EP 3083
VL 45
IS 9
DI 10.1016/j.patcog.2012.01.009
DE Structural pattern recognition; Graph embedding; Data clustering; Graph classification
AB 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.
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