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Title: Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?
Authors: Paraschiv, Ionut
Dascalu, Mihai
McNamara, Danielle
Trausan-Matu, Stefan
Keywords: learning analytics
2-mode multilayered graph
semantic similarity
Issue Date: 2016
Publisher: Springer
Citation: Paraschiv, I. C., Dascalu, M., McNamara, D.S., & Trausan-Matu, S. (2016). Finding the Needle in a Haystack: Who are the most Central Authors within a Domain? In K. Verbert, M. Sharples & T. Klobucar (Eds.), 11th European Conference on Technology Enhanced Learning (EC-TEL 2016) (pp. 632–635). Lyon, France: Springer.
Abstract: The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.
Appears in Collections:1. RAGE Publications

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