Scoring the resourcefulness of researchers using bibliographic coupling patterns

Abstract Networks constructed from citation and publication data can be mined to find top-ranking authors or papers using graph-theoretic algorithms. This article proposes an indicator called the “follow-score” that identifies which authors are the most resourceful to “follow” in terms of referencing patterns within a given body of literature. For testing purposes, we use Web of Science indexed publications under the subject category of “Information Science & Library Science” between the years 2008 and 2018 inclusive. Using the top-ranking follow-worthy authors, we search the study dataset for other similar researchers using cosine similarity.