Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks

Ingo Scholtes (), Nicolas Wider, René Pfitzner, Antonios Garas, Claudio J. Tessone and Frank Schweitzer
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Ingo Scholtes: ETH Zurich, Chair of Systems Design
Nicolas Wider: ETH Zurich, Chair of Systems Design
René Pfitzner: ETH Zurich, Chair of Systems Design
Antonios Garas: ETH Zurich, Chair of Systems Design
Claudio J. Tessone: ETH Zurich, Chair of Systems Design
Frank Schweitzer: ETH Zurich, Chair of Systems Design

Nature Communications, 2014, vol. 5, issue 1, 1-9

Abstract: Abstract Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6024

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DOI: 10.1038/ncomms6024

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