From temporal network data to the dynamics of social relationships
From temporal network data to the dynamics of social relationships
复制标题
从时间网络数据到社会关系的动态
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
N. Claidière
中科院分区：
文献类型：
--
作者：
Valeria Gelardi;A. Barrat;N. Claidière
网络是社会系统的成熟表示，时间网络被广泛用于研究其动态。时态网络数据通常由连续时间窗上的一系列静态网络组成，但其长度是任意的，不一定对应于系统的任何固有时间尺度。此外，社交网络演化的结果并不令人满意：短时间窗口包含很少的信息，而在大时间窗口上聚合则模糊了动态。因此，从时间网络到社交网络的有意义的不断发展的表示仍然是一个挑战。在这里，我们为此目的引入了一个框架：将时间网络数据转换为不断发展的加权网络，其中个体之间的链接权重在每次交互时都会更新。最重要的是，这种转变考虑到了由于个人注意力有限而导致的社会关系的相互依赖：两个人之间的每次互动不仅加强了他们的相互关系，而且削弱了他们与他人的关系。我们研究了这种转变的具体例子，并将其应用于社交互动的多个数据集。使用在学校收集的时间联系数据，我们展示了我们的框架如何突出其结构和时间组织的特殊性。然后，我们将综合扰动引入到一组狒狒交互的数据集中，以表明可以在广泛的时间尺度和参数范围内检测社会群体中的扰动。我们的框架为时间社交网络的分析带来了新的视角。
Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks.
影响因子：
16.6
作者：
Burkart, J. M.;Allon, O.;van Schaik, C. P.
通讯作者：
van Schaik, C. P.