Generating Synthetic Graph Data from Random Network Models

TLDR: In this article , the authors survey the results obtained for these so-called graph generators, which are partially based on [43 SPP 1736] and [46] . But, the results are limited in the sense that the available instances do not satisfy the needs of experimenters, especially pertaining to size and diversity.

Abstract: Abstract 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. 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. By design, network models describe graph classes and therefore can often provide meaningful synthetic instances whose applications include experimental case studies. 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. As several SPP 1736 projects engineered practical graph algorithms, multiple sampling algorithms for various graph models were designed and implemented to supplement experimental campaigns. In this chapter, we survey the results obtained for these so-called graph generators. This chapter is partially based on [43 SPP].