Dual-Channel Heterogeneous Graph Network for Author Name Disambiguation

Abstract

Name disambiguation has long been a significant issue in many fields, such as literature management and social analysis. In recent years, methods based on graph networks have performed well in name disambiguation, but these works have rarely used heterogeneous graphs to capture relationships between nodes. Heterogeneous graphs can extract more comprehensive relationship information so that more accurate node embedding can be learned. Therefore, a Dual-Channel Heterogeneous Graph Network is proposed to solve the name disambiguation problem. We use the heterogeneous graph network to capture various node information to ensure that our method can learn more accurate data structure information. In addition, we use fastText to extract the semantic information of the data. Then, a clustering method based on DBSCAN is used to classify academic papers by different authors into different clusters. In many experiments based on real datasets, our method achieved high accuracy, which proves its effectiveness.