Uddin Shahadat, Choudhury Nazim, Farhad Sardar M, Rahman Md Towfiqur
Complex Systems Research Group, Faculty of Engineering & IT, The University of Sydney, Darlington, NSW 2008, Australia.
Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, Dhaka, 1200, Bangladesh.
Sci Rep. 2017 Oct 17;7(1):13389. doi: 10.1038/s41598-017-13640-5.
The time interval between two snapshots is referred to as the window size. A given longitudinal network can be analysed from various actor-level perspectives, such as exploring how actors change their degree centrality values or participation statistics over time. Determining the optimal window size for the analysis of a given longitudinal network from different actor-level perspectives is a well-researched network science problem. Many researchers have attempted to develop a solution to this problem by considering different approaches; however, to date, no comprehensive and well-acknowledged solution that can be applied to various longitudinal networks has been found. We propose a novel approach to this problem that involves determining the correct window size when a given longitudinal network is analysed from different actor-level perspectives. The approach is based on the concept of actor-level dynamicity, which captures variability in the structural behaviours of actors in a given longitudinal network. The approach is applied to four real-world, variable-sized longitudinal networks to determine their optimal window sizes. The optimal window length for each network, determined using the approach proposed in this paper, is further evaluated via time series and data mining methods to validate its optimality. Implications of this approach are discussed in this article.
两个快照之间的时间间隔称为窗口大小。可以从各种参与者层面的角度分析给定的纵向网络，例如探究参与者如何随时间变化其度中心性值或参与统计数据。从不同参与者层面的角度确定分析给定纵向网络的最佳窗口大小是一个经过充分研究的网络科学问题。许多研究人员试图通过考虑不同方法来开发解决这个问题的方案；然而，迄今为止，尚未找到一种可应用于各种纵向网络的全面且得到广泛认可的解决方案。我们针对这个问题提出了一种新颖的方法，即在从不同参与者层面的角度分析给定纵向网络时确定正确的窗口大小。该方法基于参与者层面动态性的概念，它捕捉给定纵向网络中参与者结构行为的可变性。该方法应用于四个实际的、大小可变的纵向网络，以确定它们的最佳窗口大小。使用本文提出的方法确定的每个网络的最佳窗口长度，通过时间序列和数据挖掘方法进一步评估以验证其最优性。本文讨论了这种方法的意义。
