Analyzing massive complex networks yields promising insights about our everyday lives. Building scalable algorithms to do so is a challenging task that requires a careful analysis and an extensive evaluation. However, engineering such algorithms is often hindered by the scarcity of publicly~available~datasets. Network generators serve as a tool to alleviate this problem by providing synthetic instances with controllable parameters. However, many network generators fail to provide instances on a massive scale due to their sequential nature or resource constraints. Additionally, truly scalable network generators are few and often limited in their realism. In this work, we present novel generators for a variety of network models that are frequently used as benchmarks. By making use of pseudorandomization and divide-and-conquer schemes, our generators follow a communication-free paradigm. The resulting generators are thus embarrassingly parallel and have a near optimal scaling behavior. This allows us to generate instances of up to 243 vertices and 247 edges in less than 22 minutes on 32768 cores. Therefore, our generators allow new graph families to be used on an unprecedented scale.
@article{arxiv.1710.07565,
title = {Communication-free Massively Distributed Graph Generation},
author = {Daniel Funke and Sebastian Lamm and Ulrich Meyer and Peter Sanders and Manuel Penschuck and Christian Schulz and Darren Strash and Moritz von Looz},
journal= {arXiv preprint arXiv:1710.07565},
year = {2019}
}