Generating Scaled Replicas of Real-World Complex Networks

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.


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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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