  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.
