The structured backbone of temporal social ties

Kobayashi, Teruyoshi; Takaguchi, Taro; Barrat, Alain

doi:10.1038/s41467-018-08160-3

Cited by 54 publications

(76 citation statements)

References 41 publications

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“…[49] Earth-based experiments can utilise a much larger range of potential molecules [105] and also overcome the Boltzmann factor by using multiple lasers to first populate a high-lying initial state, then to drive the desired transition. For example, rovibronic transitions in KRb have recently [14] been used to constrain µ variation to a fractional change of less than 10 −14 /yr.…”

Section: Discussionmentioning

confidence: 99%

“…Section 2 discusses previous constraints of variation in µ cosmologically using both high abundance molecules and high sensitivity transitions. Measurements of µ variation on Earth are also being pursued, [13,14] but these constraints are much less tight than astronomical observations (on order 10 −14 /yr for Earth-based experiments vs 10 −17 /yr for astronomical observations) due to the much shorter time intervals involved.…”

Section: Introductionmentioning

confidence: 99%

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Diatomic Rovibronic Transitions as Potential Probes for Proton-to-Electron Mass Ratio Across Cosmological Time

et al. 2020

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Astrophysical molecular spectroscopy is an important method of searching for new physics through probing the variation of the proton-to-electron mass ratio, µ, with existing constraints limiting variation to a fractional change of less than 10 −17 /year. To improve on this constraint and therefore provide better guidance to theories of new physics, new molecular probes will be useful. These probes must have spectral transitions that are observable astrophysically and have different sensitivities to variation in the proton-to-electron mass ratio. Here, we concisely detail how astrophysical observations constrain the set of potential molecular probes and promising sensitive transitions based on how the frequency and intensity of these transitions align with available telescopes and observational constraints. Our detailed investigation focuses on rovibronic transitions in astrophysical diatomic molecules, using the spectroscopic models of 11 diatomics to identify sensitive transitions and probe how they generally arise in real complex molecules with many electronic states and fine structure. While none of the 11 diatomics investigated have sensitive transitions likely to be astrophysically observable, we have found that at high temperatures (1000 K) five of these diatomics have a significant number of low intensity sensitive transitions arising from an accidental near-degeneracy between vibrational levels in the ground and excited electronic state. This insight enables screening of all astrophysical diatomics as potential probes of proton-to-electron mass variation, with CN, CP, SiN and SiC being the most promising candidates for further investigation for sensitivity in rovibronic transitions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diatomic Rovibronic Transitions as Potential Probes for Proton-to-Electron Mass Ratio Across Cosmological Time

et al. 2020

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“…In this section, we summarize the EADM to detect significant links [19] and introduce the ETFM, our extension of the temporal fitness model [5], and the TDF, our extension of the well-established disparity filter [6].…”

Section: Significant Linksmentioning

confidence: 99%

“…In table 1, we summarize the acronyms used to indicate the new methodologies, the nomenclature of the article, and the parameters employed in the generation of the artificial networks. In section 2, we summarize the EADM and introduce the new approaches, namely, the ETFM (an extension of the temporal fitness model [5]) and the TDF (an extension of the disparity filter [6]). In section 3, we discuss our analytical and numerical findings for both artificial and real systems.…”

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confidence: 99%

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

2020

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Filtering information in complex networks entails the process of removing interactions explained by a proper null hypothesis and retaining the remaining interactions, which form the backbone network. The reconstructed backbone network depends upon the accuracy and reliability of the available tools, which, in turn, are affected by the specific features of the available dataset. Here, we examine the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties. In addition to the recently proposed evolving activity driven model, we extend two existing approaches (the disparity filter and the temporal fitness model) to tackle time-varying phenomena. Our analysis focuses on the influence of the network size, which was previously shown to be a determining factor for the performance of the evolving activity driven model. Through mathematical and numerical analysis, we propose general guidelines for the use of these three approaches based on the available dataset. For small networks, the evolving temporal fitness model offers a more reasonable trade-off between the number of links assigned to the backbone network and the accuracy of their inference. The main limitation of this methodology lies in its computational cost, which becomes excessively high for large networks. In this case, the evolving activity driven model could be a valid substitute to the evolving temporal fitness model. If one seeks to minimize the number of links inaccurately included in the backbone network at the risk of dismissing many links that could belong to it, then the temporal disparity filter would be the approach-of-choice. Overall, our contribution expands the toolbox of network discovery in the technical literature and should help users in choosing the right network discovery instrument, depending on the problem considered.

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“…Such "static" catalogues of patterns of firing partially fail at highlighting that the temporally coordinated firing of nodes gives rise to a dynamics of functional links 10-12 , i.e., to a temporal network [13][14][15] .The temporal network framework has recently emerged in order to take into account that for many systems, a static network representation is only a first approximation that hides very important properties. This has been made possible by the availability of temporally resolved data in communication and social networks in particular [16][17][18][19] : studies of these data have uncovered features such as broad distributions of contact or inter-contact times (burstiness) between individuals 16, 17 , multiple temporal and structural scales [19][20][21] , and a rich array of intrinsically dynamical structures that could not be unveiled within a static network framework [22][23][24][25] . Taking into account temporality has moreover been shown to have a strong impact in processes taking place on networks, in particular the propagation of diseases or of information 18,26,27 .…”

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confidence: 99%

Dynamic core periphery structure of information sharing networks in entorhinal cortex and hippocampus

Pedreschi

Bernard

Clawson

et al. 2020

Preprint

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ABSTRACTNeural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time and as a function of changing global brain state (theta vs slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity.AUTHOR SUMMARYIt is generally thought that computations performed by local brain circuits rely on complex neural processes, associated to the flexible waxing and waning of cell assemblies, i.e. ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially “static” approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of “hubness”. Some cells may share information with a multitude of other units but only in an intermittent manner, as “activists” in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute “lobbyists”. Finally, by avoiding averages over pre-imposed states, we show that within each global oscillatory state a rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.

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The structured backbone of temporal social ties

Cited by 54 publications

References 41 publications

Diatomic Rovibronic Transitions as Potential Probes for Proton-to-Electron Mass Ratio Across Cosmological Time

Diatomic Rovibronic Transitions as Potential Probes for Proton-to-Electron Mass Ratio Across Cosmological Time

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

Dynamic core periphery structure of information sharing networks in entorhinal cortex and hippocampus

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