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A network-driven study of hyperprolific authors in computer science.

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Title: A network-driven study of hyperprolific authors in computer science.
Authors: Vieira, Vinícius da F.1,2 (AUTHOR) vinicius@ufsj.edu.br, Ferreira, Carlos H. G.2,3 (AUTHOR), Almeida, Jussara M.2 (AUTHOR), Moreira, Edré2 (AUTHOR), Laender, Alberto H. F.2 (AUTHOR), Meira Jr., Wagner2 (AUTHOR), Gonçalves, Marcos André2 (AUTHOR)
Source: Scientometrics. Apr2024, Vol. 129 Issue 4, p2255-2283. 29p.
Subjects: Computer science, Research personnel, Spine
Abstract: Scientific authors' collaborations are influenced by various factors, such as their field, geographic region, and institutional role. Here we focus on a group of authors whose patterns of publications greatly deviate from the average, previously referred as hyperprolific authors. Prior studies have investigated the emergence of hyperprolific authors and their productivity. In this article, we focus on the role of coauthorships in the hyperprolific authors' publication profiles. Based on a network model that represents researchers as nodes and weighted edges as the number of collaborations between a pair of researchers, we argue that not all network edges have the same importance to characterize the existence of hyperprolific authors. As such, we filter out "sporadic" coauthorships, revealing an underlying structure composed only of edges representing consistent and repetitive collaborations, named as the network backbone. Our network-oriented methodology was applied to a dataset of Computer Science publications extracted from DBLP, covering an 11-year period from 2010 to 2020. Our experiments reveal significant topological differences between the full coauthorship networks and backbones, concerning only authors with very off-the-pattern profiles. We also show that hyperprolific authors are consistently more likely to exhibit off-the-pattern coauthorships and that an author's probability of being present in the backbone substantially increases with her topological proximity to a hyperprolific author. Finally, we investigate how authors' hyperprolific profiles correlate to their presence in the backbone. [ABSTRACT FROM AUTHOR]
Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 177251312
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A network-driven study of hyperprolific authors in computer science.
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  Data: <searchLink fieldCode="JN" term="%22Scientometrics%22">Scientometrics</searchLink>. Apr2024, Vol. 129 Issue 4, p2255-2283. 29p.
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  Data: Scientific authors' collaborations are influenced by various factors, such as their field, geographic region, and institutional role. Here we focus on a group of authors whose patterns of publications greatly deviate from the average, previously referred as hyperprolific authors. Prior studies have investigated the emergence of hyperprolific authors and their productivity. In this article, we focus on the role of coauthorships in the hyperprolific authors' publication profiles. Based on a network model that represents researchers as nodes and weighted edges as the number of collaborations between a pair of researchers, we argue that not all network edges have the same importance to characterize the existence of hyperprolific authors. As such, we filter out "sporadic" coauthorships, revealing an underlying structure composed only of edges representing consistent and repetitive collaborations, named as the network backbone. Our network-oriented methodology was applied to a dataset of Computer Science publications extracted from DBLP, covering an 11-year period from 2010 to 2020. Our experiments reveal significant topological differences between the full coauthorship networks and backbones, concerning only authors with very off-the-pattern profiles. We also show that hyperprolific authors are consistently more likely to exhibit off-the-pattern coauthorships and that an author's probability of being present in the backbone substantially increases with her topological proximity to a hyperprolific author. Finally, we investigate how authors' hyperprolific profiles correlate to their presence in the backbone. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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