{"title":"On the duration of face-to-face contacts","authors":"","doi":"10.1140/epjds/s13688-023-00444-z","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>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 <em>sociopatterns</em> 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 <em>fluctuations</em> 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.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"94 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EPJ Data Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1140/epjds/s13688-023-00444-z","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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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.
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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