Generating Scaled Replicas of Real-World Complex Networks [chapter]

Christian L. Staudt, Michael Hamann, Ilya Safro, Alexander Gutfraind, Henning Meyerhenke
2016 Studies in Computational Intelligence  
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
more » ... , 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.
doi:10.1007/978-3-319-50901-3_2 fatcat:v7nqxl4o55c6bifoxr2aa4hpzm