Compressing the chronology of a temporal network with graph commutators

TL;DR

This paper introduces a method to compress temporal network data by merging snapshots with minimal impact on dynamics, enabling efficient analysis without significant loss of information.

Contribution

The paper proposes a novel graph commutator-based approach to aggregate snapshots in temporal networks, preserving dynamics while reducing complexity.

Findings

Significant compression achieved with minimal impact on epidemic modeling accuracy.

Method effectively identifies snapshots that can be merged without altering dynamics.

Applied to real contact data, demonstrating practical utility.

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.