Generating Scaled Replicas of Real-World Complex Networks

  • Christian L. Staudt
  • Michael Hamann
  • Ilya Safro
  • Alexander Gutfraind
  • Henning Meyerhenke
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 693)


Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks can be generated by formal rules. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how models can be fitted to an original network to produce a structurally similar replica, and (c) aim for producing much larger networks than the original exemplar. In a comparative experimental study, we find ReCoN often superior to many other stateof- the-art network generation methods. Our design yields a scalable and effective tool for replicating a given network while preserving important properties at both microand macroscopic scales and (optionally) scaling the replica by orders of magnitude in size. We recommend ReCoN as a general practical method for creating realistic test data for the engineering of computational methods on networks, verification, and simulation studies. We provide scalable open-source implementations of most studied methods, including ReCoN.


Random Graph Degree Distribution Community Detection Original Network Degree Sequence 
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 International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Theoretical InformaticsKarlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.School of ComputingClemson UniversityClemsonUSA
  3. 3.Loyola University Medical CenterMaywoodUSA
  4. 4.UptakeInc.ChicagoUSA

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