Research Topic Flows in Co-Authorship Networks
- URL: http://arxiv.org/abs/2206.07980v1
- Date: Thu, 16 Jun 2022 07:45:53 GMT
- Title: Research Topic Flows in Co-Authorship Networks
- Authors: Bastian Sch\"afermeier and Johannes Hirth and Tom Hanika
- Abstract summary: We propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields.
Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information)
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 TFNs 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.
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