Exploring Graph Based Approaches for Author Name Disambiguation
This addresses the challenge of disambiguating authors in applications like literature management and social network analysis, but appears incremental as it builds on existing approaches without claiming major breakthroughs.
The paper tackles the problem of author name disambiguation in scientific literature by exploring graph-based models that leverage network structure, presenting an analysis of these models without specifying concrete results or numbers.
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.