The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Co-authorship network analysis in health research: method and potential use
Open accessAbstract
Scientific collaboration networks are a hallmark of contemporary academic research. Researchers are no longer independent players, but members of teams that bring together complementary skills and multidisciplinary approaches around common goals. Social network analysis and co-authorship networks are increasingly used as powerful tools to assess collaboration trends and to identify leading scientists and organizations. The analysis reveals the social structure of the networks by identifying actors and their connections. This article reviews the method and potential applications of co-authorship network analysis in health. The basic steps for conducting co-authorship studies in health research are described and common network metrics are presented. The application of the method is exemplified by an overview of the global research network for Chikungunya virus vaccines.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S. S., & Faust, K. (1994) · Cited by 17,961
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557 · cited 3×
“Density is a metric to measure the connectivity within the network and is defined as the percentage of the number of existing links in relation to the maximum number of possible links in a given network [13].”
BackgroundCentrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062 · cited 1×
“A node is viewed as central to the extent that it does not depend on others as intermediaries of information [41].”
BackgroundGephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675
What is research collaboration?
Katz, J. S., & Martin, B. R. (1997) · Research Policy · doi:10.1016/s0048-7333(96)00917-1 · Cited by 2,972
Coauthorship networks and patterns of scientific collaboration
Newman, M. E. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307545100 · Cited by 1,931
Pajek - Program for Large Network Analysis
Batagelj, V., & Mrvar, A. (1999) · Cited by 1,414
Social network analysis: An approach and technique for the study of information exchange
Haythornthwaite, C. (1996) · Library & Information Science Research · doi:10.1016/s0740-8188(96)90003-1 · Cited by 980
Social Networks and Health: Models, Methods, and Applications
Valente, T. W. (2010) · doi:10.1093/acprof:oso/9780195301014.001.0001 · Cited by 933
Analysing Scientific Networks Through Co-Authorship
Glänzel, W., & Schubert, A. (2006) · Handbook of Quantitative Science and Technology Research · doi:10.1007/1-4020-2755-9_12 · Cited by 818
Cited by
Analyzing Interdisciplinary Research Using Co‐Authorship Networks
Ullah, M., Shahid, A., Din, I. U., Roman, M., Assam, M., Fayaz, M., Ghadi, Y., & Aljuaid, H. (2022) · Complexity · doi:10.1155/2022/2524491 · Cited by 76
Metadata
- Authors
- Fonseca, B. D. P. F. E., Sampaio, R. B., Fonseca, M. V. D. A., & Zicker, F.
- Venue
- Health Research Policy and Systems
- Published
- 2016-04-30
- Volume
- 14
- Issue
- 1
- Pages
- 34-34
- ISSN
- 1478-4505 (ISSN-L)
- OpenAlex
- W2344469150
- Cited by
- 305
- References
- 45
- FWCI
- 10.39
- Type
- journal article
- Language
- en
- Open access
- gold
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- Fonseca, B. D. P. F. E., Sampaio, R. B., Fonseca, M. V. D. A., & Zicker, F. (2016). Co-authorship network analysis in health research: method and potential use. Health Research Policy and Systems. https://doi.org/10.1186/s12961-016-0104-5
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
