  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.
