PT Journal
AU Jaume Gibert
   Ernest Valveny
   Horst Bunke
TI Embedding of Graphs with Discrete Attributes Via Label Frequencies
SO International Journal of Pattern Recognition and Artificial Intelligence
JI IJPRAI
PY 2013
BP 1360002
EP 1360029
VL 27
IS 3
DI 10.1142/S0218001413600021
DE Discrete attributed graphs; graph embedding; graph classification
AB 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.
ER