On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner

TL;DR

This paper introduces IUAD, an unsupervised bottom-up method for author disambiguation that reconstructs collaboration networks incrementally, outperforming existing top-down approaches by focusing on stable collaborative relations.

Contribution

The paper proposes a novel incremental, unsupervised bottom-up approach for author disambiguation based on collaboration network reconstruction, addressing limitations of prior top-down methods.

Findings

IUAD achieves superior disambiguation performance on DBLP data.

The method effectively reconstructs collaboration networks with high recall.

Incremental disambiguation for new papers is efficient and accurate.

Abstract

Author disambiguation arises when different authors share the same name, which is a critical task in digital libraries, such as DBLP, CiteULike, CiteSeerX, etc. While the state-of-the-art methods have developed various paper embedding-based methods performing in a top-down manner, they primarily focus on the ego-network of a target name and overlook the low-quality collaborative relations existed in the ego-network. Thus, these methods can be suboptimal for disambiguating authors. In this paper, we model the author disambiguation as a collaboration network reconstruction problem, and propose an incremental and unsupervised author disambiguation method, namely IUAD, which performs in a bottom-up manner. Initially, we build a stable collaboration network based on stable collaborative relations. To further improve the recall, we build a probabilistic generative model to reconstruct the complete collaboration network. In addition, for newly published papers, we can incrementally judge who publish them via only computing the posterior probabilities. We have conducted extensive experiments on a large-scale DBLP dataset to evaluate IUAD. The experimental results demonstrate that IUAD not only achieves the promising performance, but also outperforms comparable baselines significantly. Codes are available at https://github.com/papergitgit/IUAD.