Model Selection Strategies for Author Disambiguation

Author disambiguation is a prerequisite for utilizing bibliographic metadata in citation analysis. Automatic disambiguation algorithms mostly rely on cluster-based disambiguation strategies for identifying unique authors given their names and publications. However, most approaches rely on knowing the correct number of unique authors a-priori, which is rarely the case in real world settings. In this publication we analyse cluster-based disambiguation strategies and develop a model selection method to estimate the number of distinct authors based on co-authorship networks. We show that, given clean textual features, the developed model selection method provides accurate guesses of the number of unique authors.