Constrained Randomisation of Weighted Networks

Abstract

We propose a Markov chain method to efficiently generate 'surrogate networks' that are random under the constraint of given vertex strengths. With these strength-preserving surrogates and with edge-weight-preserving surrogates we investigate the clustering coefficient and the average shortest path length of functional networks of the human brain as well as of the International Trade Networks. We demonstrate that surrogate networks can provide additional information about network-specific characteristics and thus help interpreting empirical weighted networks.

11 pages, 5 figures

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• Surrogate-assisted analysis of weighted functional brain networks
• Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems
• Surrogate-assisted network analysis of nonlinear time series
• Testing for the Network Small-World Property
• Randomisation Algorithms for Large Sparse Matrices
• Extrema Analysis of Node Centrality in Weighted Networks
• Nonequilibrium Thermodynamics in Measuring Carbon Footprints: Disentangling Structure and Artifact in Input-Output Accounting