  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
$2^{43}$ vertices and $2^{47}$ edges in less than 22 minutes on 32768 cores.
Therefore, our generators allow new graph families to be used on an
unprecedented scale.
