  Temporal networks are essential for modeling and understanding systems whose
behavior varies in time, from social interactions to biological systems. Often,
however, real-world data are prohibitively expensive to collect in a large
scale or unshareable due to privacy concerns. A promising way to bypass the
problem consists in generating arbitrarily large and anonymized synthetic
graphs with the properties of real-world networks, namely `surrogate networks'.
Until now, the generation of realistic surrogate temporal networks has remained
an open problem, due to the difficulty of capturing both the temporal and
topological properties of the input network, as well as their correlations, in
a scalable model. Here, we propose a novel and simple method for generating
surrogate temporal networks. Our method decomposes the input network into
star-like structures evolving in time. Then those structures are used as
building blocks to generate a surrogate temporal network. Our model vastly
outperforms current methods across multiple examples of temporal networks in
terms of both topological and dynamical similarity. We further show that beyond
generating realistic interaction patterns, our method is able to capture
intrinsic temporal periodicity of temporal networks, all with an execution time
lower than competing methods by multiple orders of magnitude. The simplicity of
our algorithm makes it easily interpretable, extendable and algorithmically
scalable.
