Kyung-Hwan Lee  Dong-Wan Choi

10.5626/JOK.2025.52.7.627

Continual Learning Graph Embedding entity importance Betweenness Centrality weighted PageRank network learning

This study proposed a novel approach to improving entity importance evaluation in continual graph embeddings by incorporating edge betweenness centrality as a weighting factor in a Weighted PageRank algorithm. By normalizing and integrating betweenness centrality, the proposed method effectively propagated entity importance while accounting for the significance of information flow through edges. Experimental results demonstrated significant performance improvements in MRR and Hit@N metrics across various datasets using the proposed method compared to existing methods. Notably, the proposed method showed enhanced learning performance after the initial snapshot in scenarios where new entities and relationships were continuously added. These findings highlight the effectiveness of leveraging edge centrality in promoting efficient and accurate learning in continual knowledge graph embeddings.