Modeling heterogeneity in networks using polynomial chaos

Karthikeyan Rajendran, Andreas C. Tsoumanis, Constantinos I. Siettos, Carlo R. Laing, Ioannis G. Kevrekidis

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)291-302
Number of pages12
JournalInternational Journal for Multiscale Computational Engineering
Issue number3
StatePublished - 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Mechanics
  • Computer Networks and Communications


  • Coarse-graining
  • Equation-free approach
  • Polynomial chaos
  • Social networks
  • UQ


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