These distributions are almost always parametrized (e.g., for the graph size or density) and typically follow implicitly from some randomized construction algorithm. Networks are commonly modeled by so-called random graphs and, therefore, represent probability distributions over the set of graphs . Research into and applications of these models are deeply intertwined with various fields of science. Network models are just that: a mathematical tool to describe and analyze realistic graphs. However, a formal grammar capturing the inner structure of the data expected to be processed is required to provide tailor-made solutions. They are fed by billions of interconnected sensors and computerized personal devices that produce enormous volumes of network data to be exploited.Ĭomputer science provides the means to face this big data challenge. Nowadays, modern computers offer sufficient storage and processing capacity to map out most aspects of human life and the world we inhabit. The practical importance of networks, however, only sky-rocketed with the advent of the information age. As such, humanity is seeking to understand their structures, rules, and implications for centuries (see also Sect. Networks are the very fabric that makes societies . In this chapter, we survey the results obtained for these so-called graph generators. As several SPP 1736 projects engineered practical graph algorithms, multiple sampling algorithms for various graph models were designed and implemented to supplement experimental campaigns. While there exist public network libraries with numerous datasets, the available instances do not fully satisfy the needs of experimenters, especially pertaining to size and diversity. From an algorithmics point of view, structural insights into networks can guide the engineering of tailor-made graph algorithms required to face the big data challenge.īy design, network models describe graph classes and therefore can often provide meaningful synthetic instances whose applications include experimental case studies. Network models are developed and used in various fields of science as their design and analysis can improve the understanding of the numerous complex systems we can observe on an everyday basis.
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