Reorderability of node-filtered order complexes

Ann Sizemore Blevins and Danielle S. Bassett
Phys. Rev. E 101, 052311 – Published 19 May 2020

Abstract

Growing graphs describe a multitude of developing processes from maturing brains to expanding vocabularies to burgeoning public transit systems. Each of these growing processes likely adheres to proliferation rules that establish an effective order of node and connection emergence. When followed, such proliferation rules allow the system to properly develop along a predetermined trajectory. But rules are rarely followed. Here we ask what topological changes in the growing graph trajectories might occur after the specific but basic perturbation of permuting the node emergence order. Specifically, we harness applied topological methods to determine which of six growing graph models exhibit topology that is robust to randomizing node order, termed global reorderability, and robust to temporally local node swaps, termed local reorderability. We find that the six graph models fall upon a spectrum of both local and global reorderability, and furthermore we provide theoretical connections between robustness to node pair ordering and robustness to arbitrary node orderings. Finally, we discuss real-world applications of reorderability analyses and suggest possibilities for designing reorderable networks.

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  • Received 8 May 2019
  • Revised 19 December 2019
  • Accepted 19 February 2020

DOI:https://doi.org/10.1103/PhysRevE.101.052311

©2020 American Physical Society

Physics Subject Headings (PhySH)

NetworksStatistical Physics & ThermodynamicsNonlinear Dynamics

Authors & Affiliations

Ann Sizemore Blevins1 and Danielle S. Bassett1,2,3,4,5,6,*

  • 1Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 2Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 3Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 4Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 5Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 6Santa Fe Institute, Santa Fe, New Mexico 87501, USA

  • *Corresponding author: dsb@seas.upenn.edu

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Vol. 101, Iss. 5 — May 2020

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