A map of approaches to temporal networks

TL;DR

This paper provides an overview of the development and key approaches in the field of temporal networks, emphasizing the importance of incorporating time into network analysis for better understanding and predictions.

Contribution

It offers a comprehensive map of the various methods, concepts, and questions that have emerged in temporal network research over the past decade.

Findings

Temporal networks require new methods and concepts.

Including time improves understanding and prediction of network dynamics.

The field has significantly evolved over the last decade.

Abstract

The study of temporal networks is motivated by the simple and important observation that just as network structure can affect dynamics, so can structure in time. Just as network topology can teach us about the system in question, so can its temporal characteristics. In many cases, leaving out either one of these components would lead to an incomplete understanding of the system or poor predictions. We argue that including time into network modeling inevitably leads researchers away from the trodden paths of network science. Temporal network theory requires something different -- new methods, new concepts, new questions -- compared to static networks. In this introductory chapter, we overview the ideas that the field of temporal networks has brought forward in the last decade. We also place the contributions to the current volume on this map of temporal-network approaches.