Highly dynamic social networks, where connectivity continuously changes in time, are becoming more and more pervasive. Knowledge mobilization, which refers to the use of knowledge toward the achievement of goals, is one of the many examples of dynamic social networks. Despite the wide use and extensive study of dynamic networks, their temporal component is often neglected in social network analysis, and statistical measures are usually performed on static network representations. As a result, measures of importance (like betweenness centrality) typically do not reveal the temporal role of the entities involved. Our goal is to contribute to fill this limitation by proposing a form of temporal betweenness measure (foremost betweenness). Our method is analytical as well as experimental: we design an algorithm to compute foremost betweenness, and we apply it to a case study to analyze a knowledge mobilization network. We propose a form of temporal betweenness measure (foremost betweenness) to analyze a knowledge mobilization network and we introduce, for the first time, an algorithm to compute exact foremost betweenness. We then show that this measure, which explicitly takes time 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 negligible, but whose temporal role might instead be important to accelerate mobilization flow in the network. We also observe the reverse behavior by detecting nodes with high static centrality, whose role as temporal bridges is instead very low. In this paper, we focus on a form of temporal betweenness designed to detect accelerators in dynamic networks. By revealing potentially important temporal roles, this study is a first step toward a better understanding of the impact of time in social networks and opens the road to further investigation.

中文翻译：

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知识动员网络中时间介数的计算与分析。

高度动态的社交网络，其连通性随时间不断变化，正变得越来越普遍。知识动员是指将知识用于实现目标，是动态社交网络的众多例子之一。尽管动态网络得到了广泛的应用和广泛的研究，但它们的时间分量在社交网​​络分析中经常被忽略，并且通常对静态网络表示进行统计测量。因此，重要性度量（如中介中心性）通常不会揭示所涉及实体的时间角色。我们的目标是通过提出一种时间介数度量（最重要的介数）来填补这一限制。我们的方法是分析性的和实验性的：我们设计了一种算法来计算最重要的介数，我们将其应用于案例研究以分析知识动员网络。我们提出了一种时间介数度量（最重要的介数）来分析知识动员网络，并且我们首次引入了一种算法来计算精确的最重要的介数。然后，我们表明，这种明确考虑时间的措施允许我们检测完全隐藏在经典统计分析中的中心性角色。特别是，我们发现了静态中心性可以忽略不计的节点，但其时间角色可能对加速网络中的动员流很重要。我们还通过检测具有高静态中心性的节点来观察反向行为，其作为时间桥梁的作用反而非常低。在本文中，我们专注于一种时间中介形式，旨在检测动态网络中的加速器。通过揭示潜在的重要时间角色，这项研究是朝着更好地理解时间在社交网络中的影响迈出的第一步，并为进一步调查开辟了道路。