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

doi:10.1007/978-3-319-45153-4_79

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 proceeding › Conference Article in proceeding › Academic › peer-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 language	English
Title of host publication	11th European Conference on Technology Enhanced Learning (EC-TEL 2016)
Subtitle of host publication	Adaptive and Adaptable Learning
Editors	Katrien Verbert, Mike Sharples, Tomasz Klobucar
Publisher	Springer
Pages	632-635
ISBN (Electronic)	978-3-319-45153-4
ISBN (Print)	978-3-319-45152-7
DOIs	https://doi.org/10.1007/978-3-319-45153-4_79
Publication status	Published - 27 Sept 2016
Externally published	Yes
Event	11th European Conference on Technology Enhanced Learning (EC-TEL 2016): Adaptive and Adaptable Learning - Lyon, France Duration: 13 Sept 2016 → 16 Sept 2016 http://ectel2016.httc.de/index.php?id=753

Publication series

Series	Lecture Notes in Computer Science
Volume	9891

Conference

Conference	11th European Conference on Technology Enhanced Learning (EC-TEL 2016)
Abbreviated title	EC-TEL 2016
Country/Territory	France
City	Lyon
Period	13/09/16 → 16/09/16
Internet address	http://ectel2016.httc.de/index.php?id=753

Keywords

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

Access to Document

10.1007/978-3-319-45153-4_79Licence: Unspecified

paraschiv2016Accepted author manuscript, 81.7 KBLicence: CC BY-NC-ND

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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). Springer. https://doi.org/10.1007/978-3-319-45153-4_79

Paraschiv, Ionut Cristian ; Dascalu, Mihai ; McNamara, Danielle S. et al. / 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, Vol. 9891).

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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",

author = "Paraschiv, {Ionut Cristian} and Mihai Dascalu and McNamara, {Danielle S.} and Stefan Trausan-Matu",

year = "2016",

month = sep,

day = "27",

doi = "10.1007/978-3-319-45153-4_79",

language = "English",

isbn = "978-3-319-45152-7",

series = "Lecture Notes in Computer Science",

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editor = "Katrien Verbert and Mike Sharples and Tomasz Klobucar",

booktitle = "11th European Conference on Technology Enhanced Learning (EC-TEL 2016)",

note = "11th European Conference on Technology Enhanced Learning (EC-TEL 2016) : Adaptive and Adaptable Learning, EC-TEL 2016 ; Conference date: 13-09-2016 Through 16-09-2016",

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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. Springer, Lecture Notes in Computer Science, vol. 9891, 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. et al.
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 proceeding › Conference Article in proceeding › Academic › peer-review