Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the graph structure in the final embedding. In the case of dynamic graphs, this problem becomes even more complex as both features and structure may change over time. The embeddings of particular nodes should remain comparable during the evolution of the graph, what can be achieved by applying an alignment procedure. This step was often applied in existing works after the node embedding was already computed. In this paper, we introduce a framework -- RAFEN -- that allows to enrich any existing node embedding method using the aforementioned alignment term and learning aligned node embedding during training time. We propose several variants of our framework and demonstrate its performance on six real-world datasets. RAFEN achieves on-par or better performance than existing approaches without requiring additional processing steps.

翻译：图形机器学习研究领域的一个关键领域是节点的学习表现。一个定义明确的节点嵌入模型应该反映最后嵌入中的节点特点和图形结构。在动态图表的情况下,这个问题变得更加复杂,因为动态图表的特点和结构可能随时间变化而变化。在图形演进过程中,特定节点的嵌入应该保持可比性,通过应用调整程序可以取得什么成果。在节点嵌入已经计算之后,这一步骤常常应用到现有的工作中。在本文中,我们引入了一个框架 -- -- RAFEN -- -- 能够利用上述校准术语来丰富任何现有的节点嵌入方法,并在培训期间学习对齐的节点嵌入。我们提出了我们框架的若干变式,并展示了它在六个真实世界数据集上的性能。RAFEN在不需要额外处理步骤的情况下,实现了平行性或更好的性能。</s>