Temporal network analysis: Introduction, methods and detailed tutorial with R

TL;DR

This paper introduces the fundamental concepts, methods, and practical tutorial for analyzing temporal networks, emphasizing their application in understanding dynamic learning processes and social interactions.

Contribution

It provides a comprehensive introduction and detailed tutorial on temporal network analysis, including building, visualizing, and analyzing networks with real-world data.

Findings

Insights into knowledge co-construction and information flow

Demonstration of temporal network analysis techniques

Practical guidance with real-world dataset analysis

Abstract

Learning involves relations, interactions and connections between learners, teachers and the world at large. Such interactions are essentially temporal and unfold in time. Yet, researchers have rarely combined the two aspects (the temporal and relational aspects) in an analytics framework. Temporal networks allow modeling of the temporal learning processes i.e., the emergence and flow of activities, communities, and social processes through fine-grained dynamic analysis. This can provide insights into phenomena like knowledge co-construction, information flow, and relationship building. This chapter introduces the basic concepts of temporal networks, their types and techniques. A detailed guide of temporal network analysis is introduced in this chapter, that starts with building the network, visualization, mathematical analysis on the node and graph level. The analysis is performed with a real-world dataset. The discussion chapter offers some extra resources for interested users who want to expand their knowledge of the technique.