Predicting citation patterns: defining and determining influence

David Guy Brizan, Kevin Gallagher, Arnab Jahangir, Theodore Brown

Abstract: Definitions for influence in bibliometrics are surveyed and expanded upon in this work. On data composed of the union of DBLP and CiteSeer x , approximately 6 million publications, a relatively small number of features are developed to describe the set, including loyalty and community longevity, two novel features. These features are successfully used to predict the influential set of papers in a series of machine learning experiments. The most predictive features are highlighted and discussed.