Tempus Fugit：时间对知识动员网络的影响
Tempus Fugit: The Impact of Time in Knowledge Mobilization Networks
摘要：
社交网络的时间成分在其分析中经常被忽略，并且统计测量通常在网络的“静态”表示上执行。因此，重要性度量（例如中介中心性）无法揭示所涉及实体的任何时间角色。我们的目标是开始填补这一限制，提出一种形式的时间介数措施，并利用它来分析知识动员网络。我们表明，这种措施，这需要明确考虑到时间，使我们能够检测中心的角色，完全隐藏在经典的统计分析。特别是，我们发现节点的静态中心被认为是微不足道的，但其时间的作用，而不是重要的，以加速网络中的动员流。我们还观察到相反的行为，通过检测具有高静态中心性的节点，其作为时间桥梁的作用是非常低的。通过揭示重要的时间角色，本研究是更好地理解时间在社交网络中的影响的第一步，并为进一步研究开辟了道路。
英文摘要：
The temporal component of social networks is often neglected in their analysis, and statistical measures are typically performed on a "static" representation of the network. As a result, measures of importance (like betweenness centrality) cannot reveal any temporal role of the entities involved. Our goal is to start filling this limitation by proposing a form of temporal betweenness measure, and by using it to analyse a knowledge mobilization network. We show that this measure, which takes time explicitly into account, allows us to detect centrality roles that were completely hidden in the classical statistical analysis. In particular, we uncover nodes whose static centrality was considered negligible, but whose temporal role is instead important to accelerate mobilization flow in the network. We also observe the reverse behaviour by detecting nodes with high static centrality, whose role as temporal bridges is instead very low. By revealing important temporal roles, this study is a first step towards a better understanding of the impact of time in social networks, and opens the road to further investigation.
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