Publication Date
2021
Document Type
Thesis
Committee Members
Tanvi Banerjee, Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Michael L. Raymer, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Link prediction in the domain of scientific collaborative networks refers to exploring and determining whether a connection between two entities in an academic network may emerge in the future. This study aims to analyze the relevance of academic collaborations and identify the factors that drive co-author relationships in a heterogeneous bibliographic network. Using topological, semantic, and graph representation learning techniques, we measure the authors' similarities w.r.t their structural and publication data to identify the reasons that promote co-authorships. Experimental results show that the proposed approach successfully infer the co-author links by identifying authors with similar research interests. Such a system can be used to recommend potential collaborations among the authors.
Page Count
101
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2021
Copyright
Copyright 2021, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0001-8769-1964
