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Verlagsausgabe
DOI: 10.5445/IR/1000082334
Veröffentlicht am 23.04.2018
Originalveröffentlichung
DOI: 10.1007/s41109-017-0054-z

Generating realistic scaled complex networks

Staudt, Christian L.; Hamann, Michael; Gutfraind, Alexander; Safro, Ilya; Meyerhenke, Henning

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 ... mehr


Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 2364-8228
URN: urn:nbn:de:swb:90-823340
KITopen ID: 1000082334
Erschienen in Applied Network Science
Band 2
Heft 1
Seiten Article: 36
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte Network generation; Multiscale modeling; Network modeling; Communities
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