关于面对面接触的持续时间

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

摘要 由于拓扑和时间方面的相互交织,对社交网络,特别是描述个人之间面对面互动的社交网络的分析非常复杂。在此,我们利用社交模式可穿戴传感器在一些截然不同的社会学环境中记录的公共数据,重新审视了这两个方面,并特别强调了接触持续时间的时间轴。众所周知,一个群体中所有互动的接触持续时间的分布是广泛的,在不同的环境下,其尾部彼此相似,但并不精确。通过分离每一对互动者,我们发现接触持续时间在平均互动时间附近的波动模式非常相似。我们在 7 个不同的数据集上观察到了这种共同的稳健行为。这表明,虽然我们交往的对象和平均交往时间在很大程度上取决于环境,但我们与某个特定个体分配比平时更多或更少的时间的倾向是不变的,即受社会环境之外的某些规则的支配。其他数据显示,狒狒群体中也存在同样的波动。我们将这种新的度量关系称为 "对比度",它可用于建立和测试基于代理的模型,或作为流行病学研究中描述长时间接触的输入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the duration of face-to-face contacts

Abstract

The analysis of social networks, in particular those describing face-to-face interactions between individuals, is complex due to the intertwining of the topological and temporal aspects. We revisit here both, using public data recorded by the sociopatterns wearable sensors in some very different sociological environments, putting particular emphasis on the contact duration timelines. As well known, the distribution of the contact duration for all the interactions within a group is broad, with tails that resemble each other, but not precisely, in different contexts. By separating each interacting pair, we find that the fluctuations of the contact duration around the mean-interaction time follow however a very similar pattern. This common robust behavior is observed on 7 different datasets. It suggests that, although the set of persons we interact with and the mean-time spent together, depend strongly on the environment, our tendency to allocate more or less time than usual with a given individual is invariant, i.e. governed by some rules that lie outside the social context. Additional data reveal the same fluctuations in a baboon population. This new metric, which we call the relation “contrast”, can be used to build and test agent-based models, or as an input for describing long duration contacts in epidemiological studies.

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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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