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March 2016 Topic-adjusted visibility metric for scientific articles
Linda S. L. Tan, Aik Hui Chan, Tian Zheng
Ann. Appl. Stat. 10(1): 1-31 (March 2016). DOI: 10.1214/15-AOAS887

Abstract

Measuring the impact of scientific articles is important for evaluating the research output of individual scientists, academic institutions and journals. While citations are raw data for constructing impact measures, there exist biases and potential issues if factors affecting citation patterns are not properly accounted for. In this work, we address the problem of field variation and introduce an article level metric useful for evaluating individual articles’ visibility. This measure derives from joint probabilistic modeling of the content in the articles and the citations among them using latent Dirichlet allocation (LDA) and the mixed membership stochastic blockmodel (MMSB). Our proposed model provides a visibility metric for individual articles adjusted for field variation in citation rates, a structural understanding of citation behavior in different fields, and article recommendations which take into account article visibility and citation patterns. We develop an efficient algorithm for model fitting using variational methods. To scale up to large networks, we develop an online variant using stochastic gradient methods and case-control likelihood approximation. We apply our methods to the benchmark KDD Cup 2003 dataset with approximately 30,000 high energy physics papers.

Citation

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Linda S. L. Tan. Aik Hui Chan. Tian Zheng. "Topic-adjusted visibility metric for scientific articles." Ann. Appl. Stat. 10 (1) 1 - 31, March 2016. https://doi.org/10.1214/15-AOAS887

Information

Received: 1 June 2015; Revised: 1 October 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

zbMATH: 1358.62114
MathSciNet: MR3480485
Digital Object Identifier: 10.1214/15-AOAS887

Keywords: Article level metric , citation network models , stochastic blockmodels , stochastic variational inference , variational Bayes

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.10 • No. 1 • March 2016
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