Data-aware Multilayer Networks.
Networks are routinely used for modelling and analysis of grouping and spreading phenomena such as epidemics, gene regulation, animal communication, all the way to the cyber-physical systems such as the internet-of-things or a network of robots. Standard network approaches often overlook the different types of interactions and interdependencies. Multilayer Networks (MLNs) simultaneously incorporate multiple layers of relationships between group members, as well as inter-layer correlations, in a natural and compact way. However, more detailed description poses challenges for the respective computational analysis, inference, and learning, and novel techniques are needed for the context of MLNs.
Tatjana Petrov, Stefano Tognazzi: Centrality-Preserving Exact Reductions of Multi-Layer Networks,
Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles (ISoLA 2020) [doi, pdf]