Temporal Fidelity in Dynamic Social Networks

TL;DR

This paper emphasizes the importance of high-temporal-resolution data in modeling dynamic social networks, demonstrating that minute-scale interactions are crucial for accurately understanding spreading processes.

Contribution

It highlights the significance of minute-to-minute interaction details in dynamic social networks and their impact on modeling spreading phenomena.

Findings

Minute-scale interactions are essential for accurate modeling.

High-resolution proximity data improves understanding of spreading processes.

Dynamic processes at multiple time scales influence social network analysis.

Abstract

It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.