# Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

## TL;DR

This paper introduces LTAI, a model that jointly captures topics, citations, and author authority in academic papers, explicitly modeling citation generation and author influence based on research topics.

## Contribution

LTAI is a novel model that explicitly models citation generation and author influence, improving accuracy in predicting words, citations, and authors over previous models.

## Key findings

- LTAI outperforms baseline models in citation and author prediction.
- LTAI achieves higher accuracy in modeling academic corpora.
- The model effectively captures topical authority in scholarly networks.

## Abstract

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author's influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.