摘要翻译：
关于人口流动的新数据来源的提供为研究社会网络、人口流动和流行病传播等动态过程之间的相互作用开辟了新的途径。在这里，我们分析了在大规模现实世界场景中个人面对面距离的时间分辨数据。我们比较了两个性质非常不同的设置，一个科学会议和一个长期运行的博物馆展览。我们跟踪面对面接近的行为网络，并从静态和动态的角度描述它们，揭示重要的差异和惊人的相似之处。我们利用我们的数据来研究传染病传播的易感-感染模型的动力学，该模型在人类邻近的动态网络上展开。会议和博物馆的传播模式明显不同，它们受到网络数据因果结构的强烈影响。对网络传播路径的深入研究表明，仅仅对静态聚集网络的认识会导致对动态网络传播路径的错误结论。
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英文标题：
《What's in a crowd? Analysis of face-to-face behavioral networks》
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作者：
Lorenzo Isella, Juliette Stehl\'e, Alain Barrat, Ciro Cattuto,
  Jean-Fran\c{c}ois Pinton, Wouter Van den Broeck
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最新提交年份：
2011
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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：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：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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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：
  The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing important differences as well as striking similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks.
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PDF链接：
https://arxiv.org/pdf/1006.1260