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摘要翻译：
数字网络、移动设备和挖掘我们在日常活动中留下的不断增加的数字痕迹的可能性正在改变我们研究人类和社会互动的方式。然而，大规模的数据集大多用于粗粒度的集体和统计行为，而关于人与人之间交互的高分辨率数据通常局限于相对较小的个人群体。在这里，我们提出了一个可扩展的实验框架，用于收集实时数据，解决面对面的社会交互，具有可调的空间和时间粒度。我们使用有源射频识别(RFID)设备，通过交换低功率无线电数据包，以分布式方式评估相互接近度。我们分析了人与人之间互动网络的动力学，这些网络是在三个不同数量级的社区规模上进行的高分辨率实验中获得的。这些数据集显示出共同的统计特性，但缺乏从20秒到几小时的特征时间尺度。连接数量与其持续时间之间的关联显示了一种有趣的超线性行为，这表明可以在连接数量和强度中定义超级连接器。利用可伸缩性和分辨率的优势，这个实验框架允许监控社会互动，发现不同背景下个人互动方式的相似性，并识别社区中超级连接器行为的模式。这些结果可能会影响我们对所有由面对面互动驱动的现象的理解，如传染性疾病和信息的传播。
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英文标题：
《Dynamics of person-to-person interactions from distributed RFID sensor
networks》
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作者：
Ciro Cattuto, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza,
Jean-Fran\c{c}ois Pinton, Alessandro Vespignani
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最新提交年份：
2010
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分类信息：
一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Computer Science 计算机科学
二级分类：Human-Computer Interaction 人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.
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PDF链接：
https://arxiv.org/pdf/1007.3680
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