Building surrogate temporal network data from observed backbones

Charley Presigny , Petter Holme , Alain Barrat (1, 2)

1 CPT - E5 Physique statistique et systèmes complexes
2 CPT - Centre de Physique Théorique - UMR 7332

In many data sets, crucial elements co-exist with non-essential ones and noise. For data represented as networks in particular, several methods have been proposed to extract a "network backbone", i.e., the set of most important links. However, the question of how the resulting compressed views of the data can effectively be used has not been tackled. Here we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular, we explore how much information about the original data need to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes. We illustrate our results using empirical temporal networks with a broad variety of structures and properties.

Mots clés: temporal networks; surrogate data; processes on networks

Domaines: Mécanique statistique [cond-mat.stat-mech]

Charley Presigny, Petter Holme, Alain Barrat. Building surrogate temporal network data from observed backbones. Physical Review E , 2021, 103 (5), ⟨10.1103/PhysRevE.103.052304⟩. ⟨hal-03219887⟩