Networks
Research themes
4. Dynamics of networks
Networks typically evolve over time. The way in which this happens is often closely related to their functionality. Random graphs are essential tools to model real-life network structures as stochastic objects that grow in time according to certain local growth rules. By adapting these rules, different types of dynamic network behavior can be captured and analyzed.
The projects related to this theme are:
- Algorithmic and combinatorial aspects of partition functions
- Exploration of sparse networks through random rooted forests
- Random walks on dynamic random graphs
- Percolation and Random Walks on Preferential Attachment Models
- Hamilton cycles in sparse graphs
- Analysis of community detection algorithms in real-world and random graphs
- Machine Learning for Organ Allocation at EuroTransplant
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Understanding superspreading phenomena of epidemics on complex networks
- The dynamics of trust networks
- Spectra of Random graphs
- Extremal, algorithmic, and enumerative problems in graph theory
- Probabilistic methods to study independent sets in hypergraphs
- Estimation of on- and off-time distributions in a dynamic Erdos-Renyi random graph
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Covers and Factors beyond Cliques