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

Generalized Median Graph via Iterative Alternate Minimizations