Bibliographic Analysis with the Citation Network Topic Model

TL;DR

This paper introduces a novel bibliographic model combining author research areas, citation networks, and paper content, using a non-parametric extension of existing topic models to improve analysis accuracy.

Contribution

It presents a new non-parametric extension of combined Poisson mixed-topic and author-topic models for bibliographic analysis, with an efficient inference algorithm.

Findings

Improved model fitting over baselines

Enhanced clustering performance

Effective analysis of CiteSeerX publications

Abstract

Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and a clustering task compared to several baselines.