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Consider the following examples in potential applications: Hence, realism may depend on both structural network features and the more subtle emerging features of the network. The first, realism, needs to consider any properties of the network that govern the domain-specific processes of interest such as system function, dynamics, and evolution. Such cases point to the importance of data-driven methods for synthesizing networks that capture both the essential features of a system and realistic variability in order to use them in such tasks as simulations, analysis, and decision making.Ī good network generator must meet two primary criteria: realism and diversity. In both examples, the systems of interest cannot be represented by a single exemplar network, but must instead be modeled as collections of networks in which the variation among them may be just as important as their common features. In another domain, the development of cybersecurity systems requires testing across diverse threat scenarios and validation across diverse network structures that are not yet known, in anticipation of the computer networks of the future ( Dunlavy et al. 2004 Keeling and Rohani 2008 Meyers et al. ![]() For example, human contact networks in the context of infectious disease spread are notoriously difficult to estimate, and thus our understanding of the dynamics and control of epidemics stems from models that make highly simplifying assumptions or simulate contact networks from incomplete or proxy data ( Eubank et al. However, high-quality, large-scale network data is often unavailable because of economic, legal, technological, or other obstacles ( Chakrabarti and Faloutsos 2006 Brase and Brown 2009). ![]() ![]() #A synthetic data generator for online social network graphs windowsNetworks are widely used to represent connections between entities, because they provide intuitive windows into the function, dynamics, and evolution of natural and man-made systems. 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. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. 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. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. #A synthetic data generator for online social network graphs verificationOutput from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. 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. ![]()
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