System Identification for Temporal Networks

Abstract

Modelling temporal networks is an open problem that has attracted researchers from a diverse range of fields. Currently, the existing modelling solutions of time-evolving graphs do not allow us to provide an accurate graph sequence. In this paper, we examine the network dynamics from a system identification perspective. We prove that any periodic graph sequence can be accurately modelled as a linear process. We propose two algorithms, called Subspace Graph Generator (SG-gen) and Linear Periodic Graph Generator (LPG-gen), for modelling periodic graph sequences and provide their performance on artificial graph sequences. We further propose a novel model, called Linear Graph Generator (LG-gen), that can be applied to non-periodic graph sequences. Our experiments on artificial and real networks demonstrate that many temporal networks can be accurately approximated by periodic graph sequences.