METRICS FOR ATTRIBUTED GRAPHS BASED ON THE MAXIMAL SIMILARITY COMMON SUBGRAPH
Džena Hidović, Marcello Pelillo · International Journal of Pattern Recognition and Artificial Intelligence · 2004
Two distance measures for attributed graphs are presented that are based on the maximal similarity common subgraph of two graphs. They are generalizations of two existing distance measures based on the maximal common subgraph. The new measures are superior to the well-known measures based on elementary edit transformations in that no particular edit operations (together with their costs) need to be defined. Moreover, they can deal not only with structural distortions, but also with perturbations of attributes. It is shown that the new distance measures are metrics.