Characterization of interactions’ persistence in time-varying networks

Abstract

Abstract Many complex networked systems exhibit volatile dynamic interactions among their vertices, whose order and persistence reverberate on the outcome of dynamical processes taking place on them. To quantify and characterize the similarity of the snapshots of a time-varying network—a proxy for the persistence,—we present a study on the persistence of the interactions based on a descriptor named temporality. We use the average value of the temporality, $$\overline{\mathcal {T}}$$ T ¯ , to assess how “special” is a given time-varying network within the configuration space of ordered sequences of snapshots. We analyse the temporality of several empirical networks and find that empirical sequences are much more similar than their randomized counterparts. We study also the effects on $$\overline{\mathcal {T}}$$ T ¯ induced by the (time) resolution at which interactions take place.