Author Name Disambiguation Based on Heterogeneous Graph

Abstract

Since multiple people share the same name in the real world, this will cause performance degradation to academic search systems and lead to misattribution of publications. The author name disambiguation algorithm has not yet to be well solved. In this paper, we propose a disambiguation method that combines heterogeneous graph-based and improved label propagation, first we construct a publication heterogeneous graph network, then graph neural networks is applied to aggregate the nodes representation and relation types, finally combined with the improved label propagation algorithm to realize clustering. The task of author name disambiguation is completed to improve the retrieval performance. Experimental results on two public datasets show that our method was improved by 2.8% and 4.9% over the suboptimal method, respectively. Our method can effectively reduce the number of publications returning the wrong author and improve the performance of the academic retrieval system.