We define a distance between temporal graphs based on graph embeddings built using time-respecting random walks. We study both the case of matched graphs, when there exists a known relation between the nodes, and the unmatched case, when such a relation is unavailable and the graphs may be of different sizes. We illustrate the interest of our distance definition, using both real and synthetic temporal network data, by showing its ability to discriminate between graphs with different structural and temporal properties. Leveraging state-of-the-art machine learning techniques, we propose an efficient implementation of distance computation that is viable for large-scale temporal graphs.

翻译：我们定义了一种基于时间敏感随机游走构建的图嵌入，用于度量时间图之间的距离。我们研究了两种情形：节点间存在已知关系的匹配图情形，以及节点关系未知且图可能具有不同规模的未匹配图情形。通过使用真实与合成时间网络数据，我们展示了该距离定义在区分具有不同结构与时间特性的图方面的能力。借助最先进的机器学习技术，我们提出了一种适用于大规模时间图的高效距离计算方法。