Revealing networks from dynamics: an introduction

Timme, Marc; Casadiego, Jose

doi:10.1088/1751-8113/47/34/343001

Cited by 178 publications

(218 citation statements)

References 96 publications

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“…Such approaches are particularly important when one needs to indirectly estimate specific parameters and functions from available data. As we will explain in forthcoming paragraphs, inferring the connectivity of networks from their nodal dynamics constitutes an important and challenging inverse problem [22].…”

Section: Introductionmentioning

confidence: 99%

“…A functional connectivity is constructed by computing correlation-based measures on the dynamics of pairs of units [22]. For example, if correlations between two units exceed a certain value, one assumes that there is a link between these two units.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, functional connectivities may not reflect the actual network structure. Also, one may measure high correlations between two units even though they are not directly connected [22]. Such high correlations may be induced by a third unit controlling the other two, or by the entire network or both.…”

Section: Introductionmentioning

confidence: 99%

“…Such high correlations may be induced by a third unit controlling the other two, or by the entire network or both. Furthermore, even the correlations of two physically connected units may be decorrelated by external noise or common external drivings [22].…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the structural connectivity is determined by the physical links existing among network units [22]. For instance, the structure of networks of spiking neurons is given by the synaptic connections existing among neurons [11,12], or in power grids, the structural connectivity is defined by the physical power lines connecting the elements in the grid [50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Network Dynamics as an Inverse Problem

Casadiego

Timme

2015

Mathematical Technology of Networks

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network Dynamics as an Inverse Problem

Casadiego

Timme

2015

Mathematical Technology of Networks

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Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

et al. 2016

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The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.

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Unravelling the backbone of climate networks from the analysis of collective dynamics and time reversal

Naghipour

Aalami

Nourani

2022

Intl Journal of Climatology

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Many complex systems are characterized by interconnected units often difficult, or even impossible, to map through direct observations, while the dynamics of units is mostly easier to measure. The reconstruction of an unknown structure from the observed dynamics is a typical inverse problem in network-based approaches to overcome such obstacles. However, this procedure is affected by the interplay between the structure and dynamics, as well as by the choice of the statistical method. In this study, collective behaviour of the Earth's climate system was investigated by focusing on three distinct climatological variables in monthly time step using a causality measure to separately construct climate networks through a robust statistical analysis. Multiplex network (MUX) was used to integrate the information obtained from collective dynamics and time reversal for better filtering spurious connectivity. In fact, the proposed approach mitigates undesired effects of pairwise statistical inference by adjusting for unphysical relationships in two layers of the constructed networks. The results indicate that the mere existence of statistical errors allowed by the hypothesis testing lead, inevitably, to reconstruct spurious connections in the null models even when the collective dynamics consists of white-noise datasets from uncoupled units. The impact of spurious connections with the MUX dynamics is not remarkable to reconstruct the underlying plausible interactions; therefore, the concepts of MUX might be employed with the statistical analysis to unravel the backbone of climate networks. In this way that the inferred connections could distinguish highly connected regions for better understanding interconnected dynamical patterns, yielding deep insights into underlying physical interactions between the oceans and atmosphere. More specifically, edge directionality features provide a complementary tool for analysing topology of the networks with respect to the climatological variables, identifying relevant patterns in the climate system.

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Revealing networks from dynamics: an introduction

Cited by 178 publications

References 96 publications

Network Dynamics as an Inverse Problem

Network Dynamics as an Inverse Problem

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

Unravelling the backbone of climate networks from the analysis of collective dynamics and time reversal

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