Common Growth Patterns for Regional Social Networks: a Point Process Approach

TL;DR

This paper investigates the macroscopic growth patterns of regional social networks, revealing invariant behaviors and modeling their link formation using point processes, with implications for understanding social network evolution.

Contribution

It introduces a point process-based model capturing the growth dynamics of regional social networks, highlighting invariant behaviors across different datasets.

Findings

Startup phase modeled by self-exciting point process

Post-startup growth modeled by non-homogeneous Poisson process

Identified daily and nightly activity patterns in link formation

Abstract

Although recent research on social networks emphasizes microscopic dynamics such as retweets and social connectivity of an individual user, we focus on macroscopic growth dynamics of social network link formation. Rather than focusing on one particular dataset, we find invariant behavior in regional social networks that are geographically concentrated. Empirical findings suggest that the startup phase of a regional network can be modeled by a self-exciting point process. After the startup phase ends, the growth of the links can be modeled by a non-homogeneous Poisson process with constant rate across the day but varying rates from day to day, plus a nightly inactive period when local users are expected to be asleep. Conclusions are drawn based on analyzing four different datasets, three of which are regional and a non-regional one is included for contrast.