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 causality-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 both betweenness and closeness centrality compared to a static Graph Convolutional Neural Network.

翻译：节点中心性在网络科学、社交网络分析和推荐系统中发挥着关键作用。在时间序列数据中，基于静态路径的中心性（如接近中心性或介数中心性）可能对时间图中节点的真实重要性产生误导性结果。为解决这一问题，学者定义了基于节点对之间最短时间尊重路径的时间泛化介数中心性和接近中心性。然而，这些泛化方法的主要问题在于此类路径的计算成本极高。针对该问题，我们研究了德布鲁因图神经网络——一种因果感知图神经网络架构——在时间序列数据中预测时间路径中心性的应用。我们在来自生物系统和社交系统的13个时间图中对方法进行实验评估，结果表明，与静态图卷积神经网络相比，该方法显著改善了对介数中心性和接近中心性的预测效果。