The hidden topological structure of flow network functionality

Abstract

The ability to reroute and control flow is vital to the function of venation networks across a wide range of organisms. By modifying individual edges in these networks, either by adjusting edge conductances or creating and destroying edges, organisms can robustly control the propagation of inputs to perform specific tasks. However, a fundamental disconnect exists between the structure and function of these networks: networks with different local architectures can perform the same functions. Here we answer the question of how structural changes at the microscopic level are able to collectively create functionality at the scale of an entire network. Using persistent homology, we analyze networks tuned to perform complex multifunctional tasks. We find that the responses of such networks encode a hidden topological structure composed of sectors of uniform pressure. Although these sectors are not apparent in the underlying network architectures, we find that they nonetheless correlate strongly with the tuned function. We conclude that the connectivity of these sectors, rather than that of the individual nodes, provides a quantitative relationship between structure and function in flow networks. Finally, we use this topological description to place a bound on the limits of task complexity.

arXiv comment5 pages (double column), 3 figures. arXiv admin note: substantial text overlap with arXiv:1901.00822

References in corpus (5)

• A roadmap for the computation of persistent homology
• Designing allostery-inspired response in mechanical networks
• The limits of multifunctionality in tunable networks
• Revealing structure-function relationships in functional flow networks via persistent homology
• Robust increase in supply by vessel dilation in globally coupled microvasculature

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