Path statistics, memory, and coarse-graining of continuous-time random walks on networks

• Published in: The Journal of chemical physics Vol. 143; no. 21; p. 214106
• Main Authors: Manhart, Michael, Kion-Crosby, Willow, Morozov, Alexandre V.
• Format: Journal Article
• Language: English
• Published: United States American Institute of Physics 07.12.2015; AIP Publishing LLC
• Subjects: Algorithms; Coarsening; Computer Simulation; Enumeration; Granulation; Lattices (mathematics); Mathematical models; Models, Biological; Models, Chemical; Models, Statistical; Networks; Organic chemistry; Physics; Programming languages; Random walk; Statistical mechanics; Statistical methods; Statistics
• ISSN: 0021-9606; 1089-7690; 1089-7690
• DOI: 10.1063/1.4935968

Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.