Memory in network flows and its effects on community detection, ranking, and spreading
(English)Manuscript (preprint) (Other academic)
Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markovapproach is used in conventional community detection, ranking, and spreading analysis although it ignores a potentially important feature ofthe dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and while weonly observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has importantconsequences for community detection, ranking, and information spreading. For example, capturing dynamics with a second-order Markovmodel allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. Thesefindings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting forhigher-order memory in network flows can help us better understand how real systems are organized and function.
Other Physics Topics
Research subject Physics
IdentifiersURN: urn:nbn:se:umu:diva-89146OAI: oai:DiVA.org:umu-89146DiVA: diva2:719090