Exploring Graph Based Approaches for Author Name Disambiguation

TL;DR

This paper investigates graph-based models for author name disambiguation, addressing challenges posed by data clutter and complex name scenarios in scientific literature management.

Contribution

It explores the use of network structure models for author disambiguation and provides an analysis of their effectiveness.

Findings

Graph-based models can effectively utilize network structure for disambiguation

Analysis reveals strengths and limitations of different graph approaches

Potential improvements for large-scale literature databases

Abstract

In many applications, such as scientific literature management, researcher search, social network analysis and etc, Name Disambiguation (aiming at disambiguating WhoIsWho) has been a challenging problem. In addition, the growth of scientific literature makes the problem more difficult and urgent. Although name disambiguation has been extensively studied in academia and industry, the problem has not been solved well due to the clutter of data and the complexity of the same name scenario. In this work, we aim to explore models that can perform the task of name disambiguation using the network structure that is intrinsic to the problem and present an analysis of the models.