Complex systems 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 paper, we propose Regulatory Temporal Interaction Network Inference (RiTINI) for inferring time-varying interaction graphs in complex systems using a novel combination of space-and-time graph attentions and graph neural ordinary differential equations (ODEs). RiTINI leverages time-lapse signals on a graph prior, as well as perturbations of signals at various nodes in order to effectively capture the dynamics of the underlying system. This approach is distinct from traditional causal inference networks, which are limited to inferring acyclic and static graphs. In contrast, RiTINI can infer cyclic, directed, and time-varying graphs, providing a more comprehensive and accurate representation of complex systems. 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. We evaluate RiTINI's performance on various simulated and real-world datasets, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.

翻译：复杂系统以实体间随时间演化的精密交互为特征。准确推断这些动态关系对于理解与预测系统行为至关重要。本文提出调控时序交互网络推断方法(RiTINI)，通过时空图注意力与图神经常微分方程(ODEs)的创新结合，推断复杂系统中的时变交互图。RiTINI利用图先验上的时间推移信号及多个节点的信号扰动，有效捕捉底层系统的动态特性。该方法区别于局限于推断无环静态图的传统因果推断网络：RiTINI能够推断含环、有向且时变的交互图，从而更全面准确地表征复杂系统。其图注意力机制使模型自适应聚焦时空中最相关的交互，而图神经ODE实现了系统动态的连续时间建模。我们在多种模拟与真实数据集上评估RiTINI性能，证明其在交互图推断方面相较于既有方法具有最先进水平。