Scoring the resourcefulness of researchers using bibliographic coupling patterns
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 p...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier
2021
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/26615/ |
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| Summary: | 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. |
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