Datum
2022-10-11Schlagwort
300 Sozialwissenschaften, Soziologie 370 Erziehung, Schul- und Bildungswesen VeröffentlichungWissenschaftliche LiteraturForschungsgegenstandAutorAutorinNetzwerkMetadata
Zur Langanzeige
Aufsatz
Research topic flows in co-authorship networks
Zusammenfassung
In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
Zitierform
In: Scientometrics Volume 128 / Issue 9 (2022-10-11) , S. 5051-5078 ; eissn:1588-2861Förderhinweis
Gefördert im Rahmen des Projekts DEALZitieren
@article{doi:10.17170/kobra-202308248679,
author={Schäfermeier, Bastian and Hirth, Johannes and Hanika, Tom},
title={Research topic flows in co-authorship networks},
journal={Scientometrics},
year={2022}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2022$n2022 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/15086 3000 Schäfermeier, Bastian 3010 Hirth, Johannes 3010 Hanika, Tom 4000 Research topic flows in co-authorship networks / Schäfermeier, Bastian 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/15086=x R 4204 \$dAufsatz 4170 5550 {{Veröffentlichung}} 5550 {{Wissenschaftliche Literatur}} 5550 {{Forschungsgegenstand}} 5550 {{Autor}} 5550 {{Autorin}} 5550 {{Netzwerk}} 7136 ##0##http://hdl.handle.net/123456789/15086
2023-09-18T12:41:50Z 2023-09-18T12:41:50Z 2022-10-11 doi:10.17170/kobra-202308248679 http://hdl.handle.net/123456789/15086 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ publication dynamics research topic flows topic models co-authorship networks 300 370 Research topic flows in co-authorship networks Aufsatz In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields. open access Schäfermeier, Bastian Hirth, Johannes Hanika, Tom doi:10.1007/s11192-022-04529-w Veröffentlichung Wissenschaftliche Literatur Forschungsgegenstand Autor Autorin Netzwerk publishedVersion eissn:1588-2861 Issue 9 Scientometrics 5051-5078 Volume 128 false
Die folgenden Lizenzbestimmungen sind mit dieser Ressource verbunden: