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Modeling heterogeneity in networks using polynomial chaos

Author(s): Rajendran, Karthikeyan; Tsoumanis, Andreas C.; Siettos, CI; Laing, CR; Kevrekidis, Yannis G.

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Abstract: © 2016 by Begell House, Inc. 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.
Publication Date: 1-Jan-2016
Citation: Rajendran, K, Tsoumanis, AC, Siettos, CI, Laing, CR, Kevrekidis, YG. (2016). Modeling heterogeneity in networks using polynomial chaos. International Journal for Multiscale Computational Engineering, 14 (3), 291 - 302. doi:10.1615/IntJMultCompEng.2016015897
DOI: doi:10.1615/IntJMultCompEng.2016015897
ISSN: 1543-1649
Pages: 291 - 302
Type of Material: Journal Article
Journal/Proceeding Title: International Journal for Multiscale Computational Engineering
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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