• Open Access

Temporal Networks: Slowing Down Diffusion by Long Lasting Interactions

Naoki Masuda, Konstantin Klemm, and Víctor M. Eguíluz
Phys. Rev. Lett. 111, 188701 – Published 29 October 2013
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Abstract

Interactions among units in complex systems occur in a specific sequential order, thus affecting the flow of information, the propagation of diseases, and general dynamical processes. We investigate the Laplacian spectrum of temporal networks and compare it with that of the corresponding aggregate network. First, we show that the spectrum of the ensemble average of a temporal network has identical eigenmodes but smaller eigenvalues than the aggregate networks. In large networks without edge condensation, the expected temporal dynamics is a time-rescaled version of the aggregate dynamics. Even for single sequential realizations, diffusive dynamics is slower in temporal networks. These discrepancies are due to the noncommutability of interactions. We illustrate our analytical findings using a simple temporal motif, larger network models, and real temporal networks.

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  • Received 13 May 2013

DOI:https://doi.org/10.1103/PhysRevLett.111.188701

This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by American Physical Society

Authors & Affiliations

Naoki Masuda1, Konstantin Klemm2, and Víctor M. Eguíluz3

  • 1Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan
  • 2Bioinformatics, Institute of Computer Science, Leipzig University, Härtelstraße 16-18, 04107 Leipzig, Germany
  • 3Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E07122 Palma de Mallorca, Spain

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Vol. 111, Iss. 18 — 1 November 2013

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