Memory in network flows and its effects on spreading dynamics and community detection

TL;DR

This paper demonstrates that incorporating second-order Markov dynamics into network flow analysis significantly improves community detection and reveals realistic travel and communication patterns, unlike traditional first-order models.

Contribution

It introduces the importance of second-order Markov models for network flows, showing their effectiveness in uncovering true system structures without additional assumptions.

Findings

Second-order models improve community detection accuracy.

Reveals actual travel patterns in air traffic.

Uncovers multidisciplinary journals in scientific communication.

Abstract

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking, and spreading analysis although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and while we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking, and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.