Complex Multilayer Networks
Natural and artificial networked systems exhibit a high level of complexity, such as simultaneous relationships or interactions, temporal variations, interdependency.
We develop mathematical models to account for this high level of complexity of networked systems. We use higher-order tensors to encode available information and to represent these integrated systems, called multilayer networks.
How multiple types of simultaneous interactions among interconnected units lead to the emergence of complex behavior, such as collective intelligence or phase transitions?
We use multilayer modeling and analysis of complex data to understand structure and dynamics of systems of systems. Our theoretical and computational studies are (mostly) based on dynamical processes (e.g., random walks) to investigate:
- Resilience of multilayer networks
- Entropy and information processing in complex (multilayer) networks
- Geometry of complex (multilayer) networks
- Mesoscale/dimensionality reduction of complex (multilayer) networks
Where do we apply multilayer network models?
We have shown that multilayer modeling often provides more insights than more traditional approaches, because they allow to naturally integrate available structural/relational/dynamical information. We successfully apply multilayer modeling and analysis to study: (selection)
- Human brains affected by neuro-degenerative diseases or psychiatric disorders
- Genotype-phenotype integration in human diseases
- Social-ecological systems’ response to climate change
- Interplay between human mobility, information awareness and epidemics spreading in developing countries
- Biological, social and socio-technical systems for the identification of functional modules/communities
- Urban transportation’s robustness to random or targeted disruptions
- Personalized routing in smart cities
