Quantifying transient spreading dynamics on networks.

Spreading phenomena on networks are essential for the collective dynamics of various natural and technological systems, from information spreading in gene regulatory networks to neural circuits and from epidemics to supply networks experiencing perturbations. Still, how local disturbances spread across networks is not yet quantitatively understood. Here, we analyze generic spreading dynamics in deterministic network dynamical systems close to a given operating point. Standard dynamical systems' theory does not explicitly provide measures for arrival times and amplitudes of a transient spreading signal because it focuses on invariant sets, invariant measures, and other quantities less relevant for transient behavior. We here change the perspective and introduce formal expectation values for deterministic dynamics to work out a theory explicitly quantifying when and how strongly a perturbation initiated at one unit of a network impacts any other. The theory provides explicit timing and amplitude information as a function of the relative position of initially perturbed and responding unit as well as depending on the entire network topology.

[1]  Stefan Kettemann,et al.  Delocalization of disturbances and the stability of ac electricity grids. , 2015, Physical review. E.

[2]  H. Stanley,et al.  Optimal paths in disordered complex networks. , 2003, Physical review letters.

[3]  Albert-László Barabási,et al.  Universality in network dynamics , 2013, Nature Physics.

[4]  Jobst Heitzig,et al.  How dead ends undermine power grid stability , 2014, Nature Communications.

[5]  Mohammad Roosta,et al.  Routing through a network with maximum reliability , 1982 .

[6]  Marc Timme,et al.  Nonlocal failures in complex supply networks by single link additions , 2013, 1305.2060.

[7]  Marc Timme,et al.  Nonlocal effects and countermeasures in cascading failures. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[9]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[10]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[11]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  T. Geisel,et al.  Random focusing of tsunami waves , 2015, Nature Physics.

[13]  Marc Timme,et al.  Topological speed limits to network synchronization. , 2003, Physical review letters.

[14]  D. Brockmann,et al.  Effective distances for epidemics spreading on complex networks , 2016, Physical review. E.

[15]  D. Helbing,et al.  The Hidden Geometry of Complex, Network-Driven Contagion Phenomena , 2013, Science.

[16]  Marc Timme,et al.  Network susceptibilities: Theory and applications. , 2016, Physical review. E.

[17]  T. Coletta,et al.  Robustness of Synchrony in Complex Networks and Generalized Kirchhoff Indices. , 2017, Physical review letters.

[18]  K. Webster,et al.  Timing of transients: quantifying reaching times and transient behavior in complex systems , 2016, 1611.07565.

[19]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  M. Timme,et al.  Critical Links and Nonlocal Rerouting in Complex Supply Networks. , 2015, Physical review letters.

[21]  Alain Barrat,et al.  Arrival time statistics in global disease spread , 2007, 0707.3047.

[22]  Marc Timme,et al.  Speed of synchronization in complex networks of neural oscillators: analytic results based on Random Matrix Theory. , 2005, Chaos.

[23]  Florian Dörfler,et al.  Optimal Placement of Virtual Inertia in Power Grids , 2015, IEEE Transactions on Automatic Control.