Compressing the chronology of a temporal network with graph commutators

Abstract

Studies of dynamics on temporal networks often represent the network as a series of "snapshots," static networks active for short durations of time. We argue that successive snapshots can be aggregated if doing so has little effect on the overlying dynamics. We propose a method to compress network chronologies by progressively combining pairs of snapshots whose matrix commutators have the smallest dynamical effect. We apply this method to epidemic modeling on real contact tracing data and find that it allows for significant compression while remaining faithful to the epidemic dynamics.

References in corpus (5)

• Dynamics of person-to-person interactions from distributed RFID sensor networks
• What's in a crowd? Analysis of face-to-face behavioral networks
• Contact patterns among high school students
• Analytical computation of the epidemic threshold on temporal networks
• Compressing network populations with modal networks reveals structural diversity

Cited by in corpus (1)

• A Parrondo paradox in susceptible-infectious-susceptible dynamics over periodic temporal networks