PT Unknown
AU Jaume Gibert
   Ernest Valveny
   Horst Bunke
   Alicia Fornes
TI On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space
BT Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop
PY 2012
BP 135
EP 143
VL 7626
DI 10.1007/978-3-642-34166-3_15
AB Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.
ER