Significant advances have been made in understanding several complex systems, such as societiest, brains, regulatory systems of cells, transportation systems,by analysing the graph structure of the networks that make up these systems. While the graph abstraction has been succesful in many ways, it fails to capture the temporal nature of the connections which are important for many of these systems. Temporal networks are generalisations for graphs, which contain the previosly often omitted time information. I will talk about how the temporal structure has major implications to spreading processes (or paths and reachability in general), and how these implications can be analysed using randomised reference models (RRMs). I will also introduce the framework of microcanonical RRMs, which allows for systematic analysis and selection of the RMMs which are now available in the growing literature on the topic. Lastly, I will present the weighted temporal event graphs representation, which contains all paths of a temporal network in a static structure. I will show how this representation can be used to study the temporal reachability in computationally efficient way, and how it allows for completely new types of analysis of large-scale temporal networks.
Mikko Kivelä received a MSc degree from Helsinki University of Technology in 2009 and a Doctor of Scienc edegree from Aalto University in 2012. He was a postdoc with the Mathematical Institute, University of Oxford until 2015, and currently holds a position as an assistant professor in the Department of Computer Science, Aalto University. His research interests are on network science and its applications, with focus on generalized network representations such as temporal networks and multilayer networks.