Generalized Median Graph via Iterative Alternate Minimizations

Nicolas Boria &
Sébastien Bougleux &
Benoit Gaüzère &
Luc Brun.

Computing a graph prototype May constitute a core element for clustering or classification tasks. However, its computation is an NP- Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.