Contrasting Global and Refined Views for Author Name Disambiguation in Heterogeneous Networks - Lima, Victor Gonçalves; Fortes, Reinaldo Silva; Ferreira, Anderson A. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Author Name Disambiguation (AND) as a clustering problem aims to correctly group publications sharing ambiguous author names into real author clusters. This work approaches the AND task using Heterogeneous Information Networks (HINs) to capture complex relationships between publications. Our method, AND-GloRe, employs a Graph Neural Network encoder trained via unsupervised contrastive learning to generate dual-view representations: a global view capturing overall relationships among publications, and a refined view capturing meta-path specific relationships. The effectiveness of our method is evaluated on the WhoIsWho datasets, achieving a pairwise F1-Score of 92.28%.