Generating fine-grained surrogate temporal networks

Understand

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.