Controlling the Multifractal Generating Measures of Complex Networks

Ruochen Yang,Paul Bogdan

doi:10.1038/s41598-020-62380-6

Abstract

Mathematical modelling of real complex networks aims to characterize their architecture and decipher their underlying principles. Self-repeating patterns and multifractality exist in many real-world complex systems such as brain, genetic, geoscience, and social networks. To better comprehend the multifractal behavior in the real networks, we propose the weighted multifractal graph model to characterize the spatiotemporal complexity and heterogeneity encoded in the interaction weights. We provide analytical tools to verify the multifractal properties of the proposed model. By varying the parameters in the initial unit square, the model can reproduce a diverse range of multifractal spectrums with different degrees of symmetry, locations, support and shapes. We estimate and investigate the weighted multifractal graph model corresponding to two real-world complex systems, namely (i) the chromosome interactions of yeast cells in quiescence and in exponential growth, and (ii) the brain networks of cognitively healthy people and patients exhibiting late mild cognitive impairment leading to Alzheimer disease. The analysis of recovered models show that the proposed random graph model provides a novel way to understand the self-similar structure of complex networks and to discriminate different network structures. Additionally, by mapping real complex networks onto multifractal generating measures, it allows us to develop new network design and control strategies, such as the minimal control of multifractal measures of real systems under different functioning conditions or states.

Highlights

Technological advances and information digitization contribute to richer complex multi-modal heterogeneous and noisy datasets in diverse fields such as social, geoscience, brain and biological networks[1,2], but at the same time call for advanced mathematical techniques for mining and investigating complex multiscale and spatiotemporal relationships
In order to generate weighted networks with multifractal characteristics and to minimally control complex networks, we introduce the weighted multifractal graph (WMG) model to generate and capture random weighted graphs with multifractal topology
We propose the weighted multifractal graph (WMG) model to capture the nodes attributes and interactions in weighted complex networks

Summary

Introduction

Technological advances and information digitization contribute to richer complex multi-modal heterogeneous and noisy datasets in diverse fields such as social, geoscience, brain and biological networks[1,2], but at the same time call for advanced mathematical techniques for mining and investigating complex multiscale and spatiotemporal relationships. Much of the complex network literature focuses on developing mathematical network models that characterize one or few pairwise interactions[3,4,5,6]. Many large-scale complex networks from sociology and biology exhibit self-similar and multifractal characteristics[13,14]. Multifractal geometric analysis makes it possible to capture the heterogeneous and multiscale interaction rules of large networks[15,16]. It efficiently characterizes large-scale complex systems[16] and can be employed to measure nodes similarity[17] and detect community structures[18]. We do want to estimate and analyze network multifractality, and want to control the multifractality and reflect the performance of robustness and resilience in complex networks

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Conclusion