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国际多尺度计算工程期刊
影响因子: 1.016 5年影响因子: 1.194 SJR: 0.554 SNIP: 0.68 CiteScore™: 1.18

ISSN 打印: 1543-1649
ISSN 在线: 1940-4352

国际多尺度计算工程期刊

DOI: 10.1615/IntJMultCompEng.2016015897
pages 291-302

MODELING HETEROGENEITY IN NETWORKS USING POLYNOMIAL CHAOS

Karthikeyan Rajendran
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ
Andreas C. Tsoumanis
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ
Constantinos I. Siettos
School of Applied Mathematics and Physical Sciences, NTUA, Athens, Greece
Carlo R. Laing
Institute for Natural and Mathematical Sciences, Massey University, Auckland, New Zealand
Ioannis G. Kevrekidis
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ; Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ

ABSTRACT

Using the dynamics of information propagation on a network as our illustrative example, we present and discuss a systematic approach to quantifying heterogeneity and its propagation that borrows established tools from uncertainty quantification, specifically, the use of polynomial chaos. The crucial assumption underlying this mathematical and computational "technology transfer" is that the evolving states of the nodes in a network quickly become correlated with the corresponding node identities: features of the nodes imparted by the network structure (e.g., the node degree, the node clustering coefficient). The node dynamics thus depend on heterogeneous (rather than uncertain) parameters, whose distribution over the network results from the network structure. Knowing these distributions allows one to obtain an efficient coarse-grained representation of the network state in terms of the expansion coefficients in suitable orthogonal polynomials. This representation is closely related to mathematical/computational tools for uncertainty quantification (the polynomial chaos approach and its associated numerical techniques). The polynomial chaos coefficients provide a set of good collective variables for the observation of dynamics on a network and, subsequently, for the implementation of reduced dynamic models of it. We demonstrate this idea by performing coarse-grained computations of the nonlinear dynamics of information propagation on our illustrative network model using the Equation-Free approach.