  Many dynamical systems can be successfully analyzed by representing them as
networks. Empirically measured networks and dynamic processes that take place
in these situations show heterogeneous, non-Markovian, and intrinsically
correlated topologies and dynamics. This makes their analysis particularly
challenging. Randomized reference models (RRMs) have emerged as a general and
versatile toolbox for studying such systems. Defined as random networks with
given features constrained to match those of an input (empirical) network, they
may, for example, be used to identify important features of empirical networks
and their effects on dynamical processes unfolding in the network. RRMs are
typically implemented as procedures that reshuffle an empirical network, making
them very generally applicable. However, the effects of most shuffling
procedures on network features remain poorly understood, rendering their use
nontrivial and susceptible to misinterpretation. Here we propose a unified
framework for classifying and understanding microcanonical RRMs (MRRMs) that
sample networks with uniform probability. Focusing on temporal networks, we
survey applications of MRRMs found in the literature, and we use this framework
to build a taxonomy of MRRMs that proposes a canonical naming convention,
classifies them, and deduces their effects on a range of important network
features. We furthermore show that certain classes of MRRMs may be applied in
sequential composition to generate new MRRMs from the existing ones surveyed in
this article. We finally provide a tutorial showing how to apply a series of
MRRMs to analyze how different network features affect a dynamic process in an
empirical temporal network.
