Cyclic Structure Induced by Load Fluctuations in Adaptive Transportation Networks

  • Erik Andreas MartensEmail author
  • Konstantin Klemm
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 30)


Transport networks are crucial to the functioning of natural systems and technological infrastructures. For flow networks in many scenarios, such as rivers or blood vessels, acyclic networks (i.e., trees) are optimal structures when assuming time-independent in- and outflow. Dropping this assumption, fluctuations of net flow at source and/or sink nodes may render the pure tree solutions unstable even under a simple local adaptation rule for conductances. Here, we consider tree-like networks under the influence of spatially heterogeneous distribution of fluctuations, where the root of the tree is supplied by a constant source and the leaves at the bottom are equipped with sinks with fluctuating loads. We find that the network divides into two regions characterized by tree-like motifs and stable cycles. The cycles emerge through transcritical bifurcations at a critical amplitude of fluctuation. For a simple network structure, depending on parameters defining the local adaptation, cycles first appear close to the leaves (or root) and then appear closer towards the root (or the leaves). The interaction between topology and dynamics gives rise to complex feedback mechanisms with many open questions in the theory of network dynamics. A general understanding of the dynamics in adaptive transport networks is essential in the study of mammalian vasculature, and adaptive transport networks may find technological applications in self-organizing piping systems.



EAM and KK acknowledge travel funding from Action CA15109, European Cooperation for Statistics of Network Data Science (COSTNET). KK acknowledges funding from MINECO through the Ramón y Cajal program and through project SPASIMM, FIS2016-80067-P (AEI/FEDER, EU). We thank J. C. Brings Jacobsen for helpful discussions on circulatory physiology and E. Katifori on adaptive networks.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Kgs. LyngbyDenmark
  2. 2.IFISC (CSIC-UIB)Campus Universitat de les Illes Balears, Palma de MallorcaSpain

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