Interactive Random Graph Generation with Evolutionary Algorithms

  • Benjamin Bach
  • Andre Spritzer
  • Evelyne Lutton
  • Jean-Daniel Fekete
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7704)


This paper introduces an interactive system called GraphCuisine that 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. In this paper we describe the graph generation process from a user’s perspective, provide details about our evolutionary algorithm, and demonstrate how GraphCuisine is employed to generate graphs that mimic a given real-world network. An interactive demo of GraphCuisine can be found on our website .


Evolutionary Algorithm Random Graph Node Degree Target Measure Representative Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Benjamin Bach
    • 1
  • Andre Spritzer
    • 2
  • Evelyne Lutton
    • 1
  • Jean-Daniel Fekete
    • 1
  1. 1.INRIAFrance
  2. 2.Universidade Federal do Rio Grande do SulBrazil

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