Hidden early-warning signals in scale-free networks

Jäger, Georg; Hofer, Christian; Kapeller, Marie Lisa; Füllsack, Manfred

doi:10.1371/journal.pone.0189853

Cited by 7 publications

(8 citation statements)

References 40 publications

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“…We considered two different network topologies, the one being an average-degree network, with nodes having a uniformly distributed number of links to other nodes. And the other is a scale-free network implemented after the suggestion of Goh et al [39], which, dependent on a parameter varying between 0 and 2 generates networks with a distribution of link degrees obeying a power law P(k) ≈ k − of which the (half of the) average link degree can be determined with a parameter m using the algorithm explained in [40]. In both topologies, the population of P = 1000 particles were differentiated with regard to centrality, taking the fraction with abovemedian centrality as "central" nodes (blue in Fig.…”

Section: Ews-analysis On a Networked Versionmentioning

confidence: 99%

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Early warning signals from the periphery

et al. 2021

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Studies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system’s components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

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Section: Ews-analysis On a Networked Versionmentioning

confidence: 99%

“…The application of detrended fluctuation analysis (DFA) and the analysis of spectral reddening (R) is explained in [7] and [40].…”

Section: Coefficient Of Variationmentioning

confidence: 99%

Early warning signals from the periphery

et al. 2021

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“…A recent study along these directions identified the presence of motifs as a cause for decreased robustness [78]. Another approach is to identify an effective state that carries information on both the topology and state of each node [79].…”

Section: Early Warning Signals For Complex Networkmentioning

confidence: 99%

“…Another major issue that plagues the detection of early warning signals in real data is the prevalence of false negatives and positives. The former can occur due to reasons such as topology effects, under-sampling or insufficient data before the transition [79,168]. The data needs to be sampled with a sampling time lower than the slowest return time in the time series [188,49] since sampling at much longer sampling times causes early warning signals to be missed [168].…”

Section: Issues and False Detections In Ewsmentioning

confidence: 99%

Early warning signals for critical transitions in complex systems

George¹,

Kachhara²,

Ambika³

2021

Preprint

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In this review, we present the different measures of early warning signals that can indicate the occurrence of critical transitions in complex systems. We start with the mechanisms that trigger critical transitions, how they relate to warning signals and the methods used to detect early warning signals (EWS) for sudden transitions or tipping. We discuss briefly a few applications in real systems in this context, like transitions in ecology, climate and environment, medicine, epidemics, finance and engineering. Towards the end, we mention the issues in detecting EWS in specific applications and our perspective on future trends in this area, especially related to sudden transitions in the dynamics of connected systems on complex networks.

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“…Unfortunately, the detection of such EWSs is not always straightforward. Sometimes the signals are hidden or obscured by topological effects [15] or the system shows a total lack of EWSs, despite featuring critical transitions [16]. This is called a false negative, since no signal can be detected, even though a critical transition is immanent.…”

Section: Introductionmentioning

confidence: 99%

Systematically false positives in early warning signal analysis

Jäger

Füllsack

2019

PLoS ONE

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Many systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic shifts in ecosystems. In order to predict imminent critical transitions, a mathematical apparatus called early warning signals has been developed and this method is used successfully in many scientific areas. However, not all critical transitions can be detected by this approach (false negative) and the appearance of early warning signals does not necessarily proof that a critical transition is imminent (false positive). Furthermore, there are whole classes of systems that always show early warning signals, even though they do not feature critical transitions. In this study we identify such classes in order to provide a safeguard against a misinterpretation of the results of an early warning signal analysis of such systems. Furthermore, we discuss strategies to avoid such systematic false positives and test our theoretical insights by applying them to real world data.

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Hidden early-warning signals in scale-free networks

Cited by 7 publications

References 40 publications

Early warning signals from the periphery

Early warning signals from the periphery

Early warning signals for critical transitions in complex systems

Systematically false positives in early warning signal analysis

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