Date of Graduation

12-2013

Document Type

Thesis

Degree Name

Master of Science in Computer Science (MS)

Degree Level

Graduate

Department

Computer Science & Computer Engineering

Advisor/Mentor

Gauch, Susan E.

Committee Member

Gauch, John M.

Second Committee Member

Beavers, M. Gordon

Keywords

Communication and the arts; Applied sciences; Social network analysis

Abstract

Finding rising stars in academia early in their careers has many implications when hiring new faculty, applying for promotion, and/or requesting grants. Typically, the impact and productivity of a researcher are assessed by a popular measurement called the h-index that grows linearly with the academic age of a researcher. Therefore, h-indices of researchers in the early stages of their careers are almost uniformly low, making it difficult to identify those who will, in future, emerge as influential leaders in their field. To overcome this problem, we make use of Social network analysis to identify young researchers most likely to become successful. We assume that the co-authorship graph reveals a great deal of information about the potential of young researchers. We built a Social network of 62,886 researchers using the data available in CiteSeerx. We then designed and trained SVM and Naïve Bayes classifiers to learn how to identify emerging authors based on the personal and Social aspects of a set of 3,200 young researchers, who had an h-index of less than or equal to four in 2005. We concluded that the success of young researchers largely depends on the number of their early citations, the number of their collaborators, and the impact and recent research activity of their collaborators.

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