Higher-order models capture changes in controllability of temporal networks

Abstract In many complex systems, elements interact via time-varying network topologies. Recent research shows that temporal correlations in the chronological ordering of interactions crucially influence properties and dynamical processes. How these affect our ability to control systems with remains unclear. this work, we use higher-order models extend framework structural controllability networks, where gives rise time-respecting paths non-Markovian characteristics. We study six empirical data sets show characteristics real can both increase or decrease minimum time needed whole system. With synthetic models, further spectral generalisations graph Laplacians networks be used analytically capture effect on controllability. Our work highlights (i) are an important source complexity significantly influences (ii) a powerful tool understand temporal-topological systems.