Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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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
Title of host publication11th European Conference on Technology Enhanced Learning (EC-TEL 2016)
Subtitle of host publicationAdaptive and Adaptable Learning
EditorsKatrien Verbert, Mike Sharples, Tomasz Klobucar
PublisherSpringer
Pages632-635
ISBN (Electronic)978-3-319-45153-4
ISBN (Print)978-3-319-45152-7
DOIs
Publication statusPublished - 27 Sep 2016
Externally publishedYes
Event11th European Conference on Technology Enhanced Learning (EC-TEL 2016): Adaptive and Adaptable Learning - Lyon, France
Duration: 13 Sep 201616 Sep 2016
http://ectel2016.httc.de/index.php?id=753

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9891

Conference

Conference11th European Conference on Technology Enhanced Learning (EC-TEL 2016)
Abbreviated titleEC-TEL 2016
CountryFrance
CityLyon
Period13/09/1616/09/16
Internet address

Fingerprint

Electric network analysis
Semantics

Keywords

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

Cite this

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): Adaptive and Adaptable Learning (pp. 632-635). (Lecture Notes in Computer Science; Vol. 9891). Springer. https://doi.org/10.1007/978-3-319-45153-4_79
Paraschiv, Ionut Cristian ; Dascalu, Mihai ; McNamara, Danielle S. ; Trausan-Matu, Stefan. / Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?. 11th European Conference on Technology Enhanced Learning (EC-TEL 2016): Adaptive and Adaptable Learning. editor / Katrien Verbert ; Mike Sharples ; Tomasz Klobucar. Springer, 2016. pp. 632-635 (Lecture Notes in Computer Science).
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title = "Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?",
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.",
keywords = "Learning analytics, 2-mode multilayered graph, Co-authorship, Co-citation, Semantic similarity",
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Paraschiv, IC, Dascalu, M, McNamara, DS & 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): Adaptive and Adaptable Learning. Lecture Notes in Computer Science, vol. 9891, Springer, pp. 632-635, 11th European Conference on Technology Enhanced Learning (EC-TEL 2016), Lyon, France, 13/09/16. https://doi.org/10.1007/978-3-319-45153-4_79

Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain? / Paraschiv, Ionut Cristian; Dascalu, Mihai; McNamara, Danielle S.; Trausan-Matu, Stefan.

11th European Conference on Technology Enhanced Learning (EC-TEL 2016): Adaptive and Adaptable Learning. ed. / Katrien Verbert; Mike Sharples; Tomasz Klobucar. Springer, 2016. p. 632-635 (Lecture Notes in Computer Science; Vol. 9891).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Paraschiv IC, Dascalu M, McNamara DS, Trausan-Matu S. Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain? In Verbert K, Sharples M, Klobucar T, editors, 11th European Conference on Technology Enhanced Learning (EC-TEL 2016): Adaptive and Adaptable Learning. Springer. 2016. p. 632-635. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-45153-4_79