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：学习节点表示一直是图机器学习研究领域的核心问题。一个定义良好的节点嵌入模型应同时反映最终嵌入中的节点特征和图结构。在动态图场景下，由于特征和结构均可能随时间变化，这一问题变得更为复杂。特定节点的嵌入在图的演化过程中应保持可比性，这可以通过应用对齐步骤实现。已有研究中，该步骤通常在节点嵌入计算完成后进行。本文提出一个框架——RAFEN——该框架利用前述对齐项，能够在训练过程中学习对齐的节点嵌入，从而增强任意现有节点嵌入方法。我们提出该框架的若干变体，并在六个真实世界数据集上验证其性能。RAFEN无需额外处理步骤即可达到与现有方法相当或更优的性能。