The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Mixed-membership models of scientific publications
Open accessAbstract
PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure of articles based only on semantic decompositions of abstracts and bibliographies and compare it with the formal discipline classifications. Our model assumes that there is a fixed number of internal categories, each characterized by multinomial distributions over words (in abstracts) and references (in bibliographies). Soft classification for each article is based on proportions of the article's content coming from each category. We discuss the appropriateness of the model for the PNAS database as well as other features of the data relevant to soft classification.
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Cited by
(2009) · Cited by 1,900
Topics over time: a non-Markov continuous-time model of topical trends
Wang, X., & McCallum, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150450 · Cited by 1,121
A Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Self-citationEfficient community detection in large networks using content and links
Ruan, Y., Fuhry, D., & Parthasarathy, S. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488483 · Cited by 304
Metadata
- Authors
- Erosheva, E., Fienberg, S., & Lafferty, J.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2004-04-06
- Volume
- 101
- Issue
- suppl_1
- Pages
- 5220-5227
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2145677303
- Cited by
- 442
- References
- 2
- FWCI
- 11.52
- Type
- journal article
- Language
- en
- Open access
- green
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- Erosheva, E., Fienberg, S., & Lafferty, J. (2004). Mixed-membership models of scientific publications. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0307760101
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
