Finding the needle in a haystack: Who are the most central authors within a domain?

Ionut Cristian Paraschiv, Mihai Dascalu, Danielle McNamara, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings
PublisherSpringer Verlag
Pages632-635
Number of pages4
Volume9891 LNCS
ISBN (Print)9783319451527
DOIs
StatePublished - 2016
Event11th European Conference on Technology Enhanced Learning, EC-TEL 2016 - Lyon, France
Duration: Sep 13 2016Sep 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9891 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th European Conference on Technology Enhanced Learning, EC-TEL 2016
CountryFrance
CityLyon
Period9/13/169/16/16

Keywords

  • 2-mode multilayered graph
  • Co-authorship
  • Co-citation
  • Learning analytics
  • Semantic similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Paraschiv, I. C., Dascalu, M., McNamara, D., & Trausan-Matu, S. (2016). Finding the needle in a haystack: Who are the most central authors within a domain? In Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings (Vol. 9891 LNCS, pp. 632-635). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9891 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-45153-4_79