Network inhomogeneities can strongly affect the critical dynamics of spreading models if the graph dimension is finite, causing large fluctuations and nonuniversal power-laws in extended control parameter spaces. This provides an alternative or an extension of self-organized criticality, observed in nature very frequently. These Griffiths Effects (GE) are generated by rare-regions and can be observed even in finite scale-free graphs. Real networks are very often modular, that may enhance GE-s and may lead to true Griffiths phases, where the fluctuations diverge. Participation in the research of GE-s in various network models with large scale simulations or by other methods would be the target of this topic. Besides spreading models, applicable to pandemics, we study synchronisation transitions from this point of view in brain and in power-grid models, where frustrated synchronization and chimera states emerge. Development of a tool, based on Kuramoto equation, for power-grid cascade faliure simualtion is planned. GPU based simulations of critical connectome models is targeted.
Investigation of heterogeneity effects in network models
Doctoral School of Physical Sciences
Doctor of HAS
Fluent in English, C programming practice, good knowledge in statistical physics