Modern temporal network theory: A colloquium

TL;DR

This paper reviews recent methods for analyzing and modeling temporal networks, emphasizing their importance in understanding dynamic processes across various fields, and discusses future research directions.

Contribution

It provides a comprehensive overview of recent developments in temporal network analysis methods, highlighting advances made in the last three years.

Findings

Enhanced understanding of spreading processes in temporal networks

Development of new modeling techniques for dynamic interactions

Identification of future research challenges in the field

Abstract

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.