# Bibliographic Analysis with the Citation Network Topic Model

arXiv: https://arxiv.org/abs/1609.06826 · HTML: https://mlsanity.com/paper/1609.06826/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics](https://mlsanity.com/paper/2204.09874/) (2022)
- [Variational Language Concepts for Interpreting Foundation Language Models](https://mlsanity.com/paper/2410.03964/) (2024)
- [One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2202.07631/) (2022)
- [Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection](https://mlsanity.com/paper/2304.06653/) (2023)
- [Disentangling Similarity and Relatedness in Topic Models](https://mlsanity.com/paper/2603.10619/) (2026)

## Abstract (arXiv)

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.