One of the most common approaches to the analysis of dynamic networks is through time-window aggregation. The resulting representation is a sequence of static networks, i.e. the snapshot graph. Despite this representation being widely used in the literature, a general framework to evaluate the soundness of snapshot graphs is still missing. In this article, we propose two scores to quantify conflicting objectives: Stability measures how much stable the sequence of snapshots is, while Fidelity measures the loss of information compared to the original data. We also develop a technique of targeted filtering of the links, to simplify the original temporal network. Our framework is tested on datasets of proximity and face-to-face interactions.
翻译:分析动态网络的最常见方法之一是通过时间窗口汇总,由此得出的表示方式是一系列静态网络,即快图。尽管文献中广泛使用这种表述方式,但仍然缺少评价快图是否健全的一般框架。在本条中,我们建议用两分数来量化相互矛盾的目标:稳定度衡量快照的顺序有多稳定,而菲德尔蒂则比原始数据衡量信息丢失的程度。我们还开发了有针对性地过滤链接的技术,以简化原始时间网络。我们的框架在近距离和面对面互动的数据集上进行了测试。