Two types of densification scaling in the evolution of temporal networks

TL;DR

This paper introduces a method to classify the origins of temporal network evolution, distinguishing between growth driven by population changes and shifts in connection probabilities, supported by empirical data and a dynamic model.

Contribution

It presents a novel classification method and a dynamic hidden-variable model to identify the sources of densification scaling in evolving temporal networks.

Findings

Real-world systems fall into two classes based on densification scaling.

The model accurately fits empirical data to identify the source of network evolution.

Empirical evidence links densification patterns to underlying dynamical processes.

Abstract

Many real-world social networks constantly change their global properties over time, such as the number of edges, size and density. While temporal and local properties of social networks have been extensively studied, the origin of their dynamical nature is not yet well understood. Networks may grow or shrink if a) the total population of nodes changes and/or b) the chance of two nodes being connected varies over time. Here, we develop a method that allows us to classify the source of time-varying nature of temporal networks. In doing so, we first show empirical evidence that real-world dynamical systems could be categorized into two classes, the difference of which is characterized by the way the number of edges grows with the number of active nodes, i.e., densification scaling. We develop a dynamic hidden-variable model to formally characterize the two dynamical classes. The model is fitted to the empirical data to identify whether the origin of scaling comes from a changing population in the system or shifts in the connecting probabilities.