%0 Journal Article
%T Embedding of Graphs with Discrete Attributes Via Label Frequencies
%A Jaume Gibert
%A Ernest Valveny
%A Horst Bunke
%J International Journal of Pattern Recognition and Artificial Intelligence
%D 2013
%V 27
%N 3
%F Jaume Gibert2013
%O DAG
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2305), last updated on Thu, 30 Jun 2016 11:25:25 +0200
%X Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient.
%K Discrete attributed graphs
%K graph embedding
%K graph classification
%U http://refbase.cvc.uab.es/files/GVB2013.pdf
%U http://dx.doi.org/10.1142/S0218001413600021
%P 1360002-1360029