TY  - JOUR
AU  - Jaume Gibert
AU  - Ernest Valveny
AU  - Horst Bunke
PY  - 2013//
TI  - Embedding of Graphs with Discrete Attributes Via Label Frequencies
T2  - IJPRAI
JO  - International Journal of Pattern Recognition and Artificial Intelligence
SP  - 1360002
EP  - 1360029
VL  - 27
IS  - 3
KW  - Discrete attributed graphs
KW  - graph embedding
KW  - graph classification
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/GVB2013.pdf
UR  - http://dx.doi.org/10.1142/S0218001413600021
N1  - DAG
ID  - Jaume Gibert2013
ER  -