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Published online on March 12, 2004, 10.1073/pnas.0307760101
PNAS | April 6, 2004 | vol. 101 | Suppl. 1 | 5220-5227


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COLLOQUIUM PAPERS
Mixed-membership models of scientific publications

Elena Erosheva * {dagger}, Stephen Fienberg {ddagger} §, and John Lafferty § ¶

*Department of Statistics, School of Social Work, and Center for Statistics and the Social Sciences, University of Washington, Seattle, WA 98195; and {ddagger}Department of Statistics, Computer Science Department, and §Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA 15213

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.

This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, "Mapping Knowledge Domains," held May 9-11, 2003, at the Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA.

{dagger} To whom correspondence should be addressed. E-mail: elena{at}stat.washington.edu.


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