Complex systems, such a gene regulatory networks and neuronal networks, are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for understanding and predicting system behavior. In this talk, I will describe a novel framework, called RiTINI, for inferring time-varying interaction graphs in complex systems using a novel combination of space-and-time attention and graph neural ODEs. The graph attention mechanism in RiTINI allows the model to adaptively focus on the most relevant interactions in time and space, while the graph neural ODEs enable continuous-time modeling of the system's dynamics. I will demonstrate RiTINI performance on various simulated and real-world single-cell datasets.