Towards a Systematic Evaluation of Generative Network Models

  • Thomas Bläsius
  • Tobias Friedrich
  • Maximilian KatzmannEmail author
  • Anton Krohmer
  • Jonathan Striebel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10836)


Generative graph models play an important role in network science. Unlike real-world networks, they are accessible for mathematical analysis and the number of available networks is not limited. The explanatory power of results on generative models, however, heavily depends on how realistic they are. We present a framework that allows for a systematic evaluation of generative network models. It is based on the question whether real-world networks can be distinguished from generated graphs with respect to certain graph parameters.

As a proof of concept, we apply our framework to four popular random graph models (Erdős-Rényi, Barabási-Albert, Chung-Lu, and hyperbolic random graphs). Our experiments for example show that all four models are bad representations for Facebook’s social networks, while Chung-Lu and hyperbolic random graphs are good representations for other networks, with different strengths and weaknesses.


Generative graph models Real-world comparison Distinguishability of network classes 



This research has received funding from the German Research Foundation (DFG) under grant agreement no. FR 2988 (ADLON, HYP).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Thomas Bläsius
    • 1
  • Tobias Friedrich
    • 1
  • Maximilian Katzmann
    • 1
    Email author
  • Anton Krohmer
    • 1
  • Jonathan Striebel
    • 1
  1. 1.Hasso Plattner InstitutePotsdamGermany

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