Dynamical Systems to Monitor Complex Networks in Continuous Time

In many settings it is appropriate to treat the evolution of pairwise
interactions over continuous time. We show that new Katz-style centrality
measures can be derived in this context via solutions to a nonautonomous ODE
driven by the network dynamics. This allows us to identify and track, at any
resolution, the most influential nodes in terms of broadcasting and receiving
information through time dependent links. In addition to the classical notion
of attenuation across edges used in the static Katz centrality measure, the ODE
also allows for attenuation over time, so that real time "running measures" can
be computed. With regard to computational efficiency, we explain why it is
cheaper to track good receivers of information than good broadcasters. We
illustrate the new measures on a large scale voice call network, where key
features are discovered that are not evident from snapshots or aggregates.