Topic-adjusted visibility metric for scientific articles

TL;DR

This paper introduces a field-adjusted visibility metric for scientific articles, using probabilistic models to account for citation biases and improve impact evaluation at the article level.

Contribution

It proposes a novel probabilistic model combining LDA and MMSB to measure article visibility adjusted for field variation, along with scalable algorithms for large datasets.

Findings

Effective adjustment for field variation in citation impact

Structural insights into citation behavior across fields

Scalable online algorithms for large citation networks

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 amongst 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.