Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betweenness can give misleading results about the true importance of nodes in a temporal graph. To address this issue, temporal generalizations of betweenness and closeness have been defined that are based on the shortest time-respecting paths between pairs of nodes. However, a major issue of those generalizations is that the calculation of such paths is computationally expensive. Addressing this issue, we study the application of De Bruijn Graph Neural Networks (DBGNN), a time-aware graph neural network architecture, to predict temporal path-based centralities in time series data. We experimentally evaluate our approach in 13 temporal graphs from biological and social systems and show that it considerably improves the prediction of betweenness and closeness centrality compared to (i) a static Graph Convolutional Neural Network, (ii) an efficient sampling-based approximation technique for temporal betweenness, and (iii) two state-of-the-art time-aware graph learning techniques for dynamic graphs.

翻译：节点中心性在网络科学、社交网络分析和推荐系统中起着关键作用。在时序数据中，基于静态路径的中心性度量（如接近中心性或中介中心性）可能对节点在时序图中的真实重要性产生误导性结果。为解决这一问题，基于节点对之间最短时间约束路径的中介中心性和接近中心性的时序泛化定义已被提出。然而，这些泛化方法的主要问题在于计算此类路径的计算成本极高。针对这一挑战，本研究探讨了德布鲁因图神经网络（DBGNN）——一种时序感知的图神经网络架构——在时序数据中预测基于时序路径的中心性度量的应用。我们在来自生物系统和社会系统的13个时序图上进行了实验评估，结果表明：相较于（i）静态图卷积神经网络，（ii）一种高效的基于采样的时序中介中心性近似计算技术，以及（iii）两种最先进的动态图时序感知图学习技术，我们的方法显著提升了对中介中心性与接近中心性的预测性能。