Understanding mechanisms behind emergent phenomena in networked systems from infrastructure networks to social networks and climate networks.
Department
Working Group
Contact
14412 Potsdam
ORCID
Nora Molkenthin studied Physics and Mathematics at Berlin, Manchester and Cambridge before obtaining a PhD on Climate Networks at PIK and HU Berlin within the interdisciplinary graduate school "Sichtbarkeit und Sichtbarmachung" in 2014. She then worked on PostDoctoral projects at MPIDS in Göttingen as well as TU Darmstadt and TU Dresden covering topics from Protein Folding to Social Networks and On-demand Ride-sharing. Since 2020 she co-leads the working group "Dynamics, stability and resilience of complex, hybrid infrastructure networks" at PIK.
- Formation and structure of complex networks
- Master-Equation formalisms for networked phenomena
- Mean-Field theory on and of networks
- On-demand Ride-sharing
- Social networks
- Protein folding
Recent Highlights:
Transdisciplinary review of Shared pooled mobility bringing together expertise from different areas:
- Creutzig & Schmaus et. al. Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda, 2024 ERL
Ride pooling:
Understanding the emergent properties of shared pooled mobility to explore its potential role in a low-carbon society.
- Molkenthin, Schroeder, Timme, Scaling laws of collective ride-sharing dynamics, 2020 PRL
- Manik, Molkenthin, Topology dependence of on-demand ride-sharing, 2020 Applied Network Science
- Zech, Molkenthin, Timme, Schroeder, Collective dynamics of capacity-constrained ride-pooling fleets, Scientific Reports 2022
Network MCMC:
Using Markow-Chain Monte-Carlo to understand the network ensembles arising from complex functional network measures.
- Pfeffer, Molkenthin, Hellmann, Ensemble analysis of complex network properties—an MCMC approach, NJP 2022
- Ansari et. al. Moving the epidemic tipping point through topologically targeted social distancing, 2021 European Physical Journal
Collective variables:
- Lücke et. al. Learning interpretable collective variables for spreading processes on networks, PRE 2024
- Lücke et.al. Large population limits of Markov processes on random networks, Stochastic Processes and their Applications 2023
For complete up-to-date list of my publications go to my Publications on Google Scholar.
