TY  - JOUR
AU  - Jaume Gibert
AU  - Ernest Valveny
AU  - Horst Bunke
PY  - 2012//
TI  - Graph Embedding in Vector Spaces by Node Attribute Statistics
T2  - PR
JO  - Pattern Recognition
SP  - 3072
EP  - 3083
VL  - 45
IS  - 9
KW  - Structural pattern recognition
KW  - Graph embedding
KW  - Data clustering
KW  - Graph classification
N2  - 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.
SN  - 0031-3203
L1  - http://refbase.cvc.uab.es/files/GVB2012a.pdf
UR  - http://dx.doi.org/10.1016/j.patcog.2012.01.009
N1  - DAG
ID  - Jaume Gibert2012
ER  -