Multiplex Networks: A Framework for Studying Multiprocess Multiscale Connectivity Via Coupled‐Network Theory With an Application to River Deltas

Tejedor, Alejandro; Longjas, Anthony; Passalacqua, Paola; Moreno, Yamir; Foufoula‐Georgiou, Efi

doi:10.1029/2018gl078355

Cited by 24 publications

(22 citation statements)

References 47 publications

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“…Our results show that the presence of directed links results in larger epidemic thresholds with respect to the case of undirected networks, and that the system is more resilient when the interlayer links are directed. Therefore, our conclusions are in line with previous works [20,21] in that directionality is a key topological feature that should not be disregarded as it can lead to new phenomenology and sizable dynamical effects.…”

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confidence: 92%

Directionality reduces the impact of epidemics in multilayer networks

Wang

Aleta

et al. 2019

New J. Phys.

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Our understanding of how diseases spread has greatly benefited from advances in network modeling. However, despite of its importance for disease contagion, the directionality of edges has rarely been taken into account. On the other hand, the introduction of the multilayer framework has made it possible to deal with more complex scenarios in epidemiology such as the interaction between different pathogens or multiple strains of the same disease. In this work, we study in depth the dynamics of disease spreading in directed multilayer networks. Using the generating function approach and numerical simulations of a stochastic susceptible-infected-susceptible model, we calculate the epidemic threshold of synthetic and real-world multilayer systems and show that it is mainly determined by the directionality of the links connecting different layers, regardless of the degree distribution chosen for the layers. Our findings are of utmost interest given the ubiquitous presence of directed multilayer networks and the widespread use of disease-like spreading processes in a broad range of phenomena such as diffusion processes in social and transportation systems.[11] can hinder disease spreading via peer punishment (e.g. prohibiting traveling if infected) and peer rewarding (e.g. free medical treatment from organizations). Both peer punishment and peer rewarding would deviate individual interactions far from symmetry, also inducing directionality in disease contagion. Even more, the own dynamics of the entities producing the diseases might be asymmetrical. For example, the interplay between cancer and the immune system can show asymmetric relationships [12]. There are also diseases with long latent periods that induce complicated dynamics between individuals who develop further the disease and those who do not, such as the interaction between individuals in the primary infection phase of Tuberculosis and those in the active state [13,14]. For those cases, multilayer networks might be able to help disentangling dynamics that would be otherwise hidden.The use of directed multilayer networks is not constrained to diseases that can infect human populations. Indeed, analogous scenarios can be found in the interface between wildlife and livestock, with diseases being endemic in one of them and then being transmitted unidirectionaly to the other [15]. This directionality is particularly relevant in the surveillance of diseases within the livestock industry, where the direction of the livestock interchange between farms can uncover structural changes that would be otherwise hidden [16]. Even more, the recent introduction of high resolution data of face-to-face interactions has also renewed the interest in using directed networks both in human and animal populations [17,18]. This data can be used to build temporal multilayer networks in which the connections between layers, i.e. different time frames, have to be necessarily directed in order to preserve the causality induced by time ordering [19].In this work, we aim at characterizing...

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supporting

confidence: 92%

Directionality reduces the impact of epidemics in multilayer networks

Wang

Aleta

et al. 2019

New J. Phys.

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“…One important recent innovation in the analysis of nonlinear systems is the use of network representations of system dynamics (Boers et al, 2015;Tejedor et al, 2018;Zhang et al, 2017). Such methods have shown that they are able to capture dynamical properties of continuous dynamical systems, such as the maximal Lyapunov exponent (McCullough et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Hypothesis Testing for Nonlinear Phenomena in the Geosciences Using Synthetic, Surrogate Data

Keylock

2019

Earth and Space Science

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Studying nonlinear and potentially chaotic phenomena in geophysics from measured signals is problematic when system noise interferes with the dynamic processes that one is trying to infer. In such circumstances, a framework for statistical hypothesis testing is necessary but the nonlinear nature of the phenomena studied makes the formulation of standard hypothesis tests, such as analysis of variance, problematic as they are based on underlying linear, Gaussian assumptions. One approach to this problem is the method of surrogate data, which is the technique explained in this paper. In particular, we focus on (i) hypothesis testing for nonlinearity by generating linearized surrogates as a null hypothesis, (ii) a variant of this that is perhaps more appropriate for image data where structural nonlinearities are common and should be retained in the surrogates, and (iii) gradual reconstruction where we systematically constrain the surrogates until there is no significant difference between data and surrogates and use this to understand geophysical processes. In addition to time series of sunspot activity, solutions to the Lorenz equations, and spatial maps of enstrophy in a turbulent channel flow, two examples are considered in detail. The first concerns gradual wavelet reconstruction testing of the significance of a specific vortical flow structure from turbulence time series acquired at a point. In the second, the degree of nonlinearity in the spatial profiles of river curvature is shown to be affected by the occurrence of meander cutoff processes but in a more complex fashion than previously envisaged.

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“…More complex network representations, such as a multiplex may help in this regard as they allow the representation of a system as a set of multiple networks in which links are allowed within each layer and between layers. This approach has been recently used to represent the transport of fluxes in channels and islands in delta systems (Tejedor et al, 2018).…”

Section: 1029/2019jf005201mentioning

confidence: 99%

Resilience of River Deltas in the Anthropocene

et al. 2020

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At a global scale, delta morphologies are subject to rapid change as a result of direct and indirect effects of human activity. This jeopardizes the ecosystem services of deltas, including protection against flood hazards, facilitation of navigation, and biodiversity. Direct manifestations of delta morphological instability include river bank failure, which may lead to avulsion, persistent channel incision or aggregation, and a change of the sedimentary regime to hyperturbid conditions. Notwithstanding the in‐depth knowledge developed over the past decades about those topics, existing understanding is fragmented, and the predictive capacity of morphodynamic models is limited. The advancement of potential resilience analysis tools may proceed from improved models, continuous observations, and the application of novel analysis techniques. Progress will benefit from synergy between approaches. Empirical and numerical models are built using field observations, and, in turn, model simulations can inform observationists about where to measure. Information theory offers a systematic approach to test the realism of alternative model concepts. Once the key mechanism responsible for a morphodynamic instability phenomenon is understood, concepts from dynamic system theory can be employed to develop early warning indicators. In the development of reliable tools to design resilient deltas, one of the first challenges is to close the sediment balance at multiple scales, such that morphodynamic model predictions match with fully independent measurements. Such a high ambition level is rarely adopted and is urgently needed to address the ongoing global changes causing sea level rise and reduced sediment input by reservoir building.

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Multiplex Networks: A Framework for Studying Multiprocess Multiscale Connectivity Via Coupled‐Network Theory With an Application to River Deltas

Cited by 24 publications

References 47 publications

Directionality reduces the impact of epidemics in multilayer networks

Directionality reduces the impact of epidemics in multilayer networks

Hypothesis Testing for Nonlinear Phenomena in the Geosciences Using Synthetic, Surrogate Data

Resilience of River Deltas in the Anthropocene

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