Event Graphs: Advances and Applications of Second-Order Time-Unfolded
Temporal Network Models

Recent advances in data collection and storage have allowed both researchers
and industry alike to collect data in real time. Much of this data comes in the
form of 'events', or timestamped interactions, such as email and social media
posts, website clickstreams, or protein-protein interactions. This of type data
poses new challenges for modelling, especially if we wish to preserve all
temporal features and structure. We propose a generalised framework to explore
temporal networks using second-order time-unfolded models, called event graphs.
Through examples we demonstrate how event graphs can be used to understand the
higher-order topological-temporal structure of temporal networks and capture
properties of the network that are unobserved when considering either a static
(or time-aggregated) model. Furthermore, we show that by modelling a temporal
network as an event graph our analysis extends easily to consider non-dyadic
interactions, known as hyper-events.