Saarland Informatics Campus
Campus E1 3, Room 325
66123 Saarbrücken, Germany
+49 (0)681 / 302-2419
I am working on numerical methods (like fast simulations or model reduction techniques) for the analysis of stochastic dynamical processes unfolding on complex networks. My research aims at understanding how stochastic interactions, that are constrained by a network topology, shape collective emerging phenomena such as epidemics, rumors, blackouts in power grids, or burst in biological neural networks.
The image shows a continuous-time SIS contagion process (infected nodes in red, healthy nodes in blue) on a geometric network.
More tools on Github.
- G. Großmann, M. Backenköhler, V. Wolf:
Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study, QEST 2020.
- G. Großmann, M. Backenköhler:
(Poster Abstract) Birth-Death Processes Reproduce the Infection Footprint of Complex Networks, HSB 2020.
- G. Großmann, L. Bortolussi, V. Wolf:
Rejection-Based Simulation of Non-Markovian Agents on Complex Networks, The 8th International Conference on Complex Networks and their Applications, 2019.
- G. Großmann, L. Bortolussi:
Reducing Spreading Processes on Networks to Markov Population Models, QEST 2019, PDF.
- G. Großmann, V. Wolf:
Rejection-Based Simulation of Stochastic Spreading Processes on Complex Networks
HSB 2019, PDF.
- C. Kyriakopoulos, G. Großmann, V. Wolf, L. Bortolussi:
Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes,
Physical Review E, 2018.
- G. Großmann, C Kyriakopoulos, L Bortolussi, V Wolf:
Lumping the Approximate Master Equation for Multistate Processes on Complex Networks,
QEST 2018, PDF.
- G. Großmann:
- Master’s Thesis: Lumping the Approximate Master Equation for Stochastic Processes on Complex Networks (avaliable upon request or at Campus-Bibliothek).
- Bachelor’s Thesis: Efficient Computation of Likelihoods in Large Markov Models (avaliable upon request or at Campus-Bibliothek).