Generating realistic scaled complex networks

Applied Network Science

Henning Meyerhenke¹  Michael Hamann¹  Christian L. Staudt¹  Alexander Gutfraind²  Ilya Safro³ 

[1] Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT);Laboratory for Mathematical Analysis of Complexity and Conflicts, University of Illinois at Chicago;School of Computing, Clemson University;

关键词: Network generation;    Multiscale modeling;    Network modeling;    Communities;   

DOI  :  10.1007/s41109-017-0054-z

来源: DOAJ

【 摘 要 】

Abstract Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. 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 ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.

【 授权许可】

Unknown