In dynamic information systems, data alignment addresses challenges like data heterogeneity, integration, and interoperability by connecting diverse datasets. To ensure the stability and effectiveness of these alignments over time, an incremental process may be required, allowing the alignments to be updated as the data evolves. While embedding-based methods are valuable for handling incremental data in the graph learning field, they are underexplored in data alignment. However, before implementing such an approach, it is essential to verify the stability of the embeddings in order to guarantee their reliability and temporal consistency. So, we study the most promising model (i.e. Node2Vec) that exhibits favourable stability in embeddings, particularly with respect to the stability of node embeddings. Despite potential variability in pairwise similarities, the idea of an incremental approach remains reliable, especially with a fixed model. Implementing such an approach can efficiently manage data dynamics in information systems with reduced resource needs. By applying this incremental process to data alignment, it will be possible to efficiently manage heterogeneous data in dynamic information system environments, while minimising resource requirements.