Identifying remarkable researchers using citation network analysis / Ephrance Abu Ujum
Experts or authorities within a research field exhibit specific traits in how they publish as well as in how they are cited by others. An analysis of such citation dependencies requires a network approach whereby a researcher’s impact depends not only on the number of citations he/she has accumulate...
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| Format: | Thesis |
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2014
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| Online Access: | http://studentsrepo.um.edu.my/4924/1/Ephrance_Abu_Ujum%2DUniversiti_Malaya%2DMSc%2D2014.pdf http://studentsrepo.um.edu.my/4924/ |
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| Summary: | Experts or authorities within a research field exhibit specific traits in how they publish as well as in how they are cited by others. An analysis of such citation dependencies requires a network approach whereby a researcher’s impact depends not only on the number of citations he/she has accumulated (over a given period of time) but also on the prominence of researchers who depend on their work. This thesis shall explore how to distinguish researchers based on temporal patterns of their publication and citation records.
As intuition may suggest, the influence of a researcher is proportional to the number of citations he/she has acquired as well as the influence of his/her citing authors. Authority can also be conferred to a researcher by virtue of his/her (co)authored works that continue to accrue citations long after the year of publication.
In this thesis, experts or authorities are identified using the “temporal citation network analysis” approach of Yang, Yin, and Davison (2011). This method assigns a high
influence score to researchers who are still actively and persistently publishing, have long publication track record, and are heavily cited (especially by influential peers).
As a case study, the method proposed by Yang and co-workers shall be used to identify authorities within the ISI Web of Knowledge category of “BUSINESS, FINANCE” spanning the period 1980-2011 inclusive. The thesis shall also explore a modification of this method to predict rising stars within the same dataset. |
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