Modeling temporal networks with bursty activity patterns of nodes and links

TL;DR

This paper introduces a temporal network model based on bursty node activation, explaining how node and link burstiness and correlations emerge, which are crucial for understanding complex dynamical systems.

Contribution

The paper presents a novel model linking node burstiness to link dynamics, capturing correlations and heavy-tailed activity distributions in temporal networks.

Findings

Node activation processes cause bursty link activity.

Correlations across links emerge from node burstiness.

Model reproduces heavy-tailed inter-event time distributions.

Abstract

The concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation and show that it leads to heavy-tailed inter-event time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems.