Dynamics of person-to-person interactions from distributed RFID sensor networks.
Dynamics of person-to-person interactions from distributed RFID sensor networks.
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DOI：
10.1371/journal.pone.0011596
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发表时间：
2010-07-15
期刊：
影响因子：
3.7
通讯作者：
Vespignani A
中科院分区：
文献类型：
--
作者：
Cattuto C;Van den Broeck W;Barrat A;Colizza V;Pinton JF;Vespignani A
数字网络、移动的设备，以及挖掘我们在日常活动中留下的越来越多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，大规模的数据集主要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仅限于相对较小的个体群体。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，解决面对面的社会互动与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程度。我们分析了在三个高分辨率的实验中获得的人与人之间的互动网络的动态进行了不同数量级的社区规模。这些数据集显示出共同的统计特性，缺乏从20秒到数小时的特征时间尺度。连接的数量和它们的持续时间之间的关联显示出有趣的超线性行为，这表明在连接的数量和强度上定义超级连接器的可能性。利用可扩展性和分辨率，这个实验框架允许监控社会互动，揭示个人在不同背景下互动方式的相似性，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面对面互动驱动的所有现象的理解，例如传染病和信息的传播。
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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DOI：
10.1126/science.1167742
发表时间：
2009-02-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Lazer D;Pentland A;Adamic L;Aral S;Barabasi AL;Brewer D;Christakis N;Contractor N;Fowler J;Gutmann M;Jebara T;King G;Macy M;Roy D;Van Alstyne M
通讯作者：
Van Alstyne M
影响因子：
--
作者：
Eagle, Nathan;Pentland, Alex (Sandy)
通讯作者：
Pentland, Alex (Sandy)
DOI：
10.1073/pnas.0610245104
发表时间：
2007-05-01
影响因子：
11.1
作者：
Onnela, J.-P.;Saramaki, J.;Barabasi, A.-L.
通讯作者：
Barabasi, A.-L.
DOI：
10.1073/pnas.0405728101
发表时间：
2004-10-05
影响因子：
11.1
作者：
Eckmann, JP;Moses, E;Sergi, D
通讯作者：
Sergi, D
影响因子：
56.9
作者：
Kossinets, G;Watts, DJ
通讯作者：
Watts, DJ