Recently, the median graph has been shown to be a good choice to obtain a representative of a given set of graphs. It has been successfully applied to graph-based classification and clustering. In this paper we exploit a theoretical property of the median, which has not yet been utilized in the past, to derive a new iterative algorithm for approximate median graph computation. Experiments done using five different graph databases show that the proposed approach yields, in four out of these five datasets, better medians than two of the previous existing methods.

Peer Reviewed