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Complex systems are characterized by intricate interactions between entitiesthat evolve dynamically over time. Accurate inference of these dynamicrelationships is crucial for understanding and predicting system behavior. Inthis paper, we propose Regulatory Temporal Interaction Network Inference(RiTINI) for inferring time-varying interaction graphs in complex systems usinga novel combination of space-and-time graph attentions and graph neuralordinary differential equations (ODEs). RiTINI leverages time-lapse signals ona graph prior, as well as perturbations of signals at various nodes in order toeffectively capture the dynamics of the underlying system. This approach isdistinct from traditional causal inference networks, which are limited toinferring acyclic and static graphs. In contrast, RiTINI can infer cyclic,directed, and time-varying graphs, providing a more comprehensive and accuraterepresentation of complex systems. The graph attention mechanism in RiTINIallows the model to adaptively focus on the most relevant interactions in timeand space, while the graph neural ODEs enable continuous-time modeling of thesystem's dynamics. We evaluate RiTINI's performance on various simulated andreal-world datasets, demonstrating its state-of-the-art capability in inferringinteraction graphs compared to previous methods.

Inferring dynamic regulatory interaction graphs from time series data with perturbations