Validating analysis time window

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.When assigning a time span to the window of a longitudinal network, the underlying principle is that actors within the network must have sufficient time to initiate network processes, such as the formation and dissolution of ties.Actors in a longitudinal network usually exhibit different rates for different network activities (e.g., the formation of new ties or the dissolution of existing ties).Implications of this approach are discussed in this article.Owing to the immense growth, proliferation, and availability of longitudinal network data and their inherently dynamic nature, it is imperative to develop a scalable and data-intrinsic architecture to analyse them effectively.The design of a longitudinal study involves the selection of a window size before or after data collection.

This attribute affords researchers the freedom to use different window sizes (e.g., a daily or weekly communication network) for their longitudinal studies based on the types of data involved.

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.

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.

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