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 Citation (Scopus)

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

Fingerprint

Cohesion
Citations
Network Analysis
Electric network analysis
Semantics
Semantic Similarity
Graph in graph theory
Ranking
Integrate
Learning

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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

Finding the needle in a haystack : Who are the most central authors within a domain? / Paraschiv, Ionut Cristian; Dascalu, Mihai; McNamara, Danielle; Trausan-Matu, Stefan.

Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings. Vol. 9891 LNCS Springer Verlag, 2016. p. 632-635 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9891 LNCS).

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

Paraschiv, IC, 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9891 LNCS, Springer Verlag, pp. 632-635, 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Lyon, France, 9/13/16. https://doi.org/10.1007/978-3-319-45153-4_79
Paraschiv IC, Dascalu M, McNamara D, Trausan-Matu S. 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. Springer Verlag. 2016. p. 632-635. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-45153-4_79
Paraschiv, Ionut Cristian ; Dascalu, Mihai ; McNamara, Danielle ; Trausan-Matu, Stefan. / Finding the needle in a haystack : Who are the most central authors within a domain?. Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings. Vol. 9891 LNCS Springer Verlag, 2016. pp. 632-635 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{19710c01c6314828bc0ee36eed689b74,
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 = "2-mode multilayered graph, Co-authorship, Co-citation, Learning analytics, Semantic similarity",
author = "Paraschiv, {Ionut Cristian} and Mihai Dascalu and Danielle McNamara and Stefan Trausan-Matu",
year = "2016",
doi = "10.1007/978-3-319-45153-4_79",
language = "English (US)",
isbn = "9783319451527",
volume = "9891 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "632--635",
booktitle = "Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings",
address = "Germany",

}

TY - GEN

T1 - Finding the needle in a haystack

T2 - Who are the most central authors within a domain?

AU - Paraschiv, Ionut Cristian

AU - Dascalu, Mihai

AU - McNamara, Danielle

AU - Trausan-Matu, Stefan

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - 2-mode multilayered graph

KW - Co-authorship

KW - Co-citation

KW - Learning analytics

KW - Semantic similarity

UR - http://www.scopus.com/inward/record.url?scp=84988453120&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84988453120&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-45153-4_79

DO - 10.1007/978-3-319-45153-4_79

M3 - Conference contribution

AN - SCOPUS:84988453120

SN - 9783319451527

VL - 9891 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 632

EP - 635

BT - Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings

PB - Springer Verlag

ER -