Overlapping Community Discovery for Identifying Key Research Themes
- Publisher:
- Institute of Electrical and Electronics Engineers
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Engineering Management, 2021, 68, (5), pp. 1321-1333
- Issue Date:
- 2021
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Overlapping_Community_Discovery_for_Identifying_Key_Research_Themes.pdf | Published version | 4.21 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Identifying key research themes is an effective way to chart knowledge structures in a field of research and, in turn, stimulate new ideas and innovation. Most thematic analyses of a research field are based on some form of network analysis, e.g., citations and cowords, and most of these networks are made up of cohesive, highly overlapping groups of nodes. Based on the suggestion that the “universal features” of networks are to be found in these overlapping communities, we argue that these same communities in a keyword network should reveal the key research themes in a field of study. With no traditional method with which to test our theory, we combined a cluster percolation algorithm with a Word2Vec model, and in a case study on information science, we were not only able to detect the overlapping communities in a keyword similarity network, but we also found a new perspective on the importance of overlapping communities as a way to identify a field's key research themes.
Please use this identifier to cite or link to this item: