Abstract：
Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs\' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model\'s explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.
摘要：
对网络组件之间的动态交互进行建模对于揭示复杂网络的演化机制至关重要。最近,时空图学习方法在表征节点间关系（INR）的动态变化方面取得了值得注意的成果。然而,挑战依然存在：INR的空间邻域开发不足，INRs动态变化中的时空依赖性被忽视，忽略了历史状态和地方信息的影响。此外,该模型的可解释性一直没有得到充分研究。为了解决这些问题,我们提出了一个可解释的时空图进化学习（ESTGEL）模型来对INR的动态演化进行建模。具体来说,提出了一种边缘注意模块，以在多级上利用INR的空间邻域，即，通过分解初始节点关系图得出的嵌套子图的层次结构。随后，提出了一个动态关系学习模块来捕获INR的时空依赖性。然后将INR用作相邻信息以改善节点表示，从而全面描绘了网络的动态演变。最后,该方法得到了大脑发育研究的真实数据的验证。动态脑网络分析的实验结果表明，在整个开发过程中，脑功能网络从分散过渡到更收敛和模块化的结构。在与包括情绪控制在内的功能相关的动态功能连接（dFC）中观察到显着变化，决策,和语言处理。
