Predicting Dynamics on Networks Hardly Depends on the Topology

TL;DR

This paper demonstrates that accurately predicting future network dynamics does not require knowledge of the true network topology, as alternative inferred networks can produce equivalent dynamic outcomes.

Contribution

It introduces a prediction algorithm that infers an intermediate network, showing that accurate dynamics prediction is possible even with topologically dissimilar inferred networks.

Findings

Accurate future dynamics prediction is achievable without the true network topology.

Inferred networks can differ topologically from the true network yet produce similar dynamics.

The network inference process is highly ill-conditioned, making exact inference impractical.

Abstract

Processes on networks consist of two interdependent parts: the network topology, consisting of the links between nodes, and the dynamics, specified by some governing equations. This work considers the prediction of the future dynamics on an unknown network, based on past observations of the dynamics. For a general class of governing equations, we propose a prediction algorithm which infers the network as an intermediate step. Inferring the network is impossible in practice, due to a dramatically ill-conditioned linear system. Surprisingly, a highly accurate prediction of the dynamics is possible nonetheless: Even though the inferred network has no topological similarity with the true network, both networks result in practically the same future dynamics.