人类面对面互动的时间网络
Temporal networks of face-to-face human interactions
摘要：
移动技术和无处不在的服务的不断增加的采用允许以前所未有的细节和规模来感知人类行为。可穿戴传感器正在以面对面接近的最佳分辨率打开人类流动和接近的新窗口。因此，描述社会和行为网络的经验数据正在获得一个纵向维度，这给分析和建模带来了新的挑战。在这里，我们回顾了最近的工作，面对面的人类接近的时间网络的表示和分析，基于大规模的数据集收集的背景下的SocioPatterns合作。我们表明，原始的行为数据可以在不同层次的粗粒度，这原来是相互补充，每个级别暴露不同的功能的底层系统进行研究。我们简要回顾了一个生成模型的时间接触网络，再现了一些统计观测。然后，我们将我们的重点从表面的统计特征的经验时间网络的动力学过程。我们讨论了如何简单的动态过程可以被用来作为探针，暴露的相互作用模式，如突发性和因果约束的重要功能。我们表明，在经验时间网络上模拟动态过程可以揭示数据集之间的差异，否则这些数据集在统计上看起来相似。此外，我们认为，由于人类动态的时间异质性，为了研究传播过程的时间特性，可能有必要放弃挂钟时间的概念，转而采用每个单独节点的内在时间概念，根据其活动水平定义。最后，我们强调了几个开放的研究问题所提出的性质的数据在手。
英文摘要：
The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.
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