Random walk on temporal networks with lasting edges

Résultats de recherche: Contribution à un journal/une revueArticle

13 Downloads (Pure)

Résumé

We consider random walks on dynamical networks where edges appear and disappear during finite time intervals. The process is grounded on three independent stochastic processes determining the walker's waiting time, the up time, and the down time of the edges. We first propose a comprehensive analytical and numerical treatment on directed acyclic graphs. Once cycles are allowed in the network, non-Markovian trajectories may emerge, remarkably even if the walker and the evolution of the network edges are governed by memoryless Poisson processes. We then introduce a general analytical framework to characterize such non-Markovian walks and validate our findings with numerical simulations.
langue originaleAnglais
Numéro d'article052307
Nombre de pages16
journalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume98
Numéro de publication5
Les DOIs
étatPublié - 20 nov. 2018

Empreinte digitale

random walk
Random walk
poisson process
downtime
Directed Acyclic Graph
stochastic processes
Poisson process
Waiting Time
Walk
Stochastic Processes
trajectories
Trajectory
intervals
Cycle
Numerical Simulation
cycles
Interval
simulation

Citer ceci

@article{5ced33c3d523430cb751827ac1fd9610,
title = "Random walk on temporal networks with lasting edges",
abstract = "We consider random walks on dynamical networks where edges appear and disappear during finite time intervals. The process is grounded on three independent stochastic processes determining the walker's waiting time, the up time, and the down time of the edges. We first propose a comprehensive analytical and numerical treatment on directed acyclic graphs. Once cycles are allowed in the network, non-Markovian trajectories may emerge, remarkably even if the walker and the evolution of the network edges are governed by memoryless Poisson processes. We then introduce a general analytical framework to characterize such non-Markovian walks and validate our findings with numerical simulations.",
keywords = "continuous time random walk, time varying network, diffusion and random walk, complex networks",
author = "Julien Petit and Martin Gueuning and Timoteo Carletti and Ben Lauwens and Renaud Lambiotte",
year = "2018",
month = "11",
day = "20",
doi = "https://doi.org/10.1103/PhysRevE.98.052307",
language = "English",
volume = "98",
journal = "Physical Review E - Statistical, Nonlinear, and Soft Matter Physics",
issn = "1539-3755",
publisher = "American Physical Society",
number = "5",

}

Random walk on temporal networks with lasting edges. / Petit, Julien; Gueuning, Martin; Carletti, Timoteo; Lauwens, Ben; Lambiotte, Renaud.

Dans: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol 98, Numéro 5, 052307, 20.11.2018.

Résultats de recherche: Contribution à un journal/une revueArticle

TY - JOUR

T1 - Random walk on temporal networks with lasting edges

AU - Petit, Julien

AU - Gueuning, Martin

AU - Carletti, Timoteo

AU - Lauwens, Ben

AU - Lambiotte, Renaud

PY - 2018/11/20

Y1 - 2018/11/20

N2 - We consider random walks on dynamical networks where edges appear and disappear during finite time intervals. The process is grounded on three independent stochastic processes determining the walker's waiting time, the up time, and the down time of the edges. We first propose a comprehensive analytical and numerical treatment on directed acyclic graphs. Once cycles are allowed in the network, non-Markovian trajectories may emerge, remarkably even if the walker and the evolution of the network edges are governed by memoryless Poisson processes. We then introduce a general analytical framework to characterize such non-Markovian walks and validate our findings with numerical simulations.

AB - We consider random walks on dynamical networks where edges appear and disappear during finite time intervals. The process is grounded on three independent stochastic processes determining the walker's waiting time, the up time, and the down time of the edges. We first propose a comprehensive analytical and numerical treatment on directed acyclic graphs. Once cycles are allowed in the network, non-Markovian trajectories may emerge, remarkably even if the walker and the evolution of the network edges are governed by memoryless Poisson processes. We then introduce a general analytical framework to characterize such non-Markovian walks and validate our findings with numerical simulations.

KW - continuous time random walk

KW - time varying network

KW - diffusion and random walk

KW - complex networks

UR - https://arxiv.org/pdf/1809.02540.pdf

UR - http://www.mendeley.com/research/random-walk-temporal-networks-lasting-edges

UR - http://www.scopus.com/inward/record.url?scp=85057206219&partnerID=8YFLogxK

U2 - https://doi.org/10.1103/PhysRevE.98.052307

DO - https://doi.org/10.1103/PhysRevE.98.052307

M3 - Article

VL - 98

JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

SN - 1539-3755

IS - 5

M1 - 052307

ER -