  Most empirical studies of complex networks do not return direct, error-free
measurements of network structure. Instead, they typically rely on indirect
measurements that are often error-prone and unreliable. A fundamental problem
in empirical network science is how to make the best possible estimates of
network structure given such unreliable data. In this paper we describe a fully
Bayesian method for reconstructing networks from observational data in any
format, even when the data contain substantial measurement error and when the
nature and magnitude of that error is unknown. The method is introduced through
pedagogical case studies using real-world example networks, and specifically
tailored to allow straightforward, computationally efficient implementation
with a minimum of technical input. Computer code implementing the method is
publicly available.
