Persistence and periodicity in a dynamic proximity network

TL;DR

This paper investigates the temporal evolution of a dynamic proximity social network, revealing broad time scale variations, strong periodicities, and biases introduced by snapshot-based analysis, emphasizing the importance of choosing an appropriate natural time scale.

Contribution

It demonstrates the presence of multiple time scales and periodicities in dynamic social networks and highlights biases from snapshot methods, proposing a natural time scale for better analysis.

Findings

Network topology evolves over broad time scales

Strong periodicities driven by external calendar cycles

Snapshot methods can bias network structure estimates

Abstract

The topology of social networks can be understood as being inherently dynamic, with edges having a distinct position in time. Most characterizations of dynamic networks discretize time by converting temporal information into a sequence of network "snapshots" for further analysis. Here we study a highly resolved data set of a dynamic proximity network of 66 individuals. We show that the topology of this network evolves over a very broad distribution of time scales, that its behavior is characterized by strong periodicities driven by external calendar cycles, and that the conversion of inherently continuous-time data into a sequence of snapshots can produce highly biased estimates of network structure. We suggest that dynamic social networks exhibit a natural time scale \Delta_{nat}, and that the best conversion of such dynamic data to a discrete sequence of networks is done at this natural rate.