Research Collaboration Analysis Using Text and Graph Features

Drahomira Herrmannova; Petr Knoth; Christopher Stahl; Robert Patton; Jack Wells

doi:10.1007/978-3-031-23804-8_33

Research Collaboration Analysis Using Text and Graph Features

Drahomira Herrmannova, Petr Knoth, Christopher Stahl, Robert Patton, Jack Wells

Learning Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how much have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset [11], a new dataset containing two types of publications – publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers
Editors	Alexander Gelbukh
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	431-441
Number of pages	11
ISBN (Print)	9783031238031
DOIs	https://doi.org/10.1007/978-3-031-23804-8_33
State	Published - 2023
Event	19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018 - Hanoi, Viet Nam Duration: Mar 18 2018 → Mar 24 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13397 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018
Country/Territory	Viet Nam
City	Hanoi
Period	03/18/18 → 03/24/18

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DEAC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Acknowledgments. This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education.

Keywords

Collaboration networks
Publication impact
Semantic similarity
Semantometrics
Text mining

Access to Document

10.1007/978-3-031-23804-8_33

Cite this

Herrmannova, D., Knoth, P., Stahl, C., Patton, R., & Wells, J. (2023). Research Collaboration Analysis Using Text and Graph Features. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers (pp. 431-441). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13397 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23804-8_33

Herrmannova, Drahomira ; Knoth, Petr ; Stahl, Christopher et al. / Research Collaboration Analysis Using Text and Graph Features. Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. editor / Alexander Gelbukh. Springer Science and Business Media Deutschland GmbH, 2023. pp. 431-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Research Collaboration Analysis Using Text and Graph Features",

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Herrmannova, D, Knoth, P, Stahl, C , Patton, R & Wells, J 2023, Research Collaboration Analysis Using Text and Graph Features. in A Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13397 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 431-441, 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, Hanoi, Viet Nam, 03/18/18. https://doi.org/10.1007/978-3-031-23804-8_33

Research Collaboration Analysis Using Text and Graph Features. / Herrmannova, Drahomira; Knoth, Petr; Stahl, Christopher et al.
Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. ed. / Alexander Gelbukh. Springer Science and Business Media Deutschland GmbH, 2023. p. 431-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13397 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how much have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset [11], a new dataset containing two types of publications – publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information.

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Herrmannova D, Knoth P, Stahl C , Patton R, Wells J. Research Collaboration Analysis Using Text and Graph Features. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 431-441. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-23804-8_33