5 papers
Spectral clustering of time-evolving networks using spatio-temporal random walks
Filip BlaÅ¡koviÄ, Tim O. F. Conrad, Stefan Klus +1
Temporal (or time-evolving) networks provide a natural framework for modeling complex systems with time-dependent interactions, where understanding the evolution of community struc…
Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds
Liam Llamazares-Elias, Samir Llamazares-Elias, Jonas Latz +1
Global information about dynamical systems can be extracted by analysing associated infinite-dimensional transfer operators, such as Perron--Frobenius and Koopman operators as well…
Random walk based snapshot clustering for detecting community dynamics in temporal networks
Filip BlaÅ¡koviÄ, Tim O. F. Conrad, Stefan Klus +1
The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented…
Clustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplacian
Maia Trower, Nataša Djurdjevac Conrad, Stefan Klus
Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and…
Dynamical systems and complex networks: A Koopman operator perspective
Stefan Klus, Nataša Djurdjevac Conrad
The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept$\unicode{x2013}$representing highly nonlinear dynamical sy…