Graph embedding aims to learn a representation of graphs' nodes in a latent low-dimensional space. The purpose is to encode the graph's structural information. While the majority of real-world networks are dynamic, literature generally focuses on static networks and overlooks evolution patterns. In a previous article entitled "TemporalNode2vec: Temporal Node Embedding in Temporal Networks", we introduced a dynamic graph embedding method that learns continuous time-aware vertex representations. In this paper, we adapt TemporalNode2vec to tackle especially the node classification-related tasks. Overall, we prove that task-specific embedding improves data efficiency significantly comparing to task-agnostic embedding.