Towards a temporal network analysis of interactive WiFi users

TL;DR

This paper introduces a temporal network analysis of WiFi user interactions in a university, revealing how time-ordered contact data significantly differ from static models and impact understanding of human contact dynamics.

Contribution

It presents a novel temporal contact network constructed from WiFi logs, highlighting the importance of time order in human interaction analysis and potential superspreader identification.

Findings

Temporal contact networks differ from static aggregation in key features.

Time order influences the relationship between path length and duration.

Temporal analysis aids in identifying potential superspreaders.

Abstract

Complex networks are used to depict topological features of complex systems. The structure of a network characterizes the interactions among elements of the system, and facilitates the study of many dynamical processes taking place on it. In previous investigations, the topological infrastructure underlying dynamical systems is simplified as a static and invariable skeleton. However, this assumption cannot cover the temporal features of many time-evolution networks, whose components are evolving and mutating. In this letter, utilizing the log data of WiFi users in a Chinese university campus, we infuse the temporal dimension into the construction of dynamical human contact network. By quantitative comparison with the traditional aggregation approach, we find that the temporal contact network differs in many features, e.g., the reachability, the path length distribution. We conclude that the correlation between temporal path length and duration is not only determined by their definitions, but also influenced by the microdynamical features of human activities under certain social circumstance as well. The time order of individuals' interaction events plays a critical role in understanding many dynamical processes via human close proximity interactions studied in this letter. Besides, our study also provides a promising measure to identify the potential superspreaders by distinguishing the nodes functioning as the relay hub.