Participants : Évelyne Lutton [correspondant] , Benjamin Bach, André Spritzer, Jean-Daniel Fekete.
GraphCuisine lets users steer an Evolutionary Algorithm (EA) to create random graphs that match user-specified measures. Generating random graphs with particular characteristics is crucial for evaluating graph algorithms, layouts and visualization techniques. Current random graph generators provide limited control of the final characteristics of the graphs they generate. The situation is even harder when one wants to generate random graphs similar to a given one, all-in-all leading to a long iterative process that involves several steps of random graph generation, parameter changes, and visual inspection. Our system follows an approach based on interactive evolutionary computation. Fitting generator parameters to create graphs with pre-defined measures is an optimization problem, while assessing the quality of the resulting graphs often involves human subjective judgment. GraphCuisine has been proved to be able to generate graphs that mimic a given real-world network. http://www.aviz.fr/Research/Graphcuisine