  Random graph (RG) models play a central role in the complex networks
analysis. They help to understand, control, and predict phenomena occurring,
for instance, in social networks, biological networks, the Internet, etc.
  Despite a large number of RG models presented in the literature, there are
few concepts underlying them. Instead of trying to classify a wide variety of
very dispersed models, we capture and describe concepts they exploit
considering preferential attachment, copying principle, hyperbolic geometry,
recursively defined structure, edge switching, Monte Carlo sampling, etc. We
analyze RG models, extract their basic principles, and build a taxonomy of
concepts they are based on. We also discuss how these concepts are combined in
RG models and how they work in typical applications like benchmarks, null
models, and data anonymization.
