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- 10386981 type article assertion.
- 10386981 type FAIRDigitalObject assertion.
- 10386981 label "Finding Potential Research Collaborations from Social Networks Derived from Topic Models" assertion.
- 10386981 comment "Community detection is a valuable tool for analyzing social networks given its potential for identifying groups with common characteristics and common interests. In this work, we focused on detecting scholarly communities based on researchers’ publication data to discover interdisciplinary collaboration recommendations (researchers working on different domains). Specifically, instead of using any physical or direct relationship between researchers, we utilized a topic model to obtain the topic-based similarity between researchers to construct the social network graph. Next, we employed an edge-weakening procedure to alter the initially constructed network to uncover a more refined community structure. Two community detection algorithms, Louvain and Spectral clustering, were utilized to find the community structures in the modified network. The results of our experiments revealed the ability to discover possible research communities for both algorithms that were comparable, which suggests that our method has the potential for identifying hidden interdisciplinary research collaboration recommendations using topical relationships as the basis for building and analyzing the social network graph." assertion.
- 10386981 creator 0000-0001-8726-8226 assertion.
- 10386981 creator 0000-0002-3588-6257 assertion.
- 10386981 creator 0000-0001-9487-5622 assertion.
- 10386981 subject topic_3316 assertion.
- 10386981 publisher 01n002310 assertion.
- 10386981 startDate "2022" assertion.
- 10386981 endDate "2024-01-17" assertion.
- 10386981 hasMetadata RAhvb8lJ7SzMDWbxu5qGSo8USZayaGtr6o8sQn46mIggE assertion.
- 10386981 contactPoint "john.sheppard@montana.edu" assertion.
- 10386981 funder 021nxhr62 assertion.