# GINopic: Topic Modeling with Graph Isomorphism Network

arXiv: https://arxiv.org/abs/2404.02115 · HTML: https://mlsanity.com/paper/2404.02115/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Neural Topic Modeling by Incorporating Document Relationship Graph](https://mlsanity.com/paper/2009.13972/) (2020)
- [Online Nonnegative Matrix Factorization with Outliers](https://mlsanity.com/paper/1604.02634/) (2016)
- [Improving Contextualized Topic Models with Negative Sampling](https://mlsanity.com/paper/2303.14951/) (2023)
- [Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling](https://mlsanity.com/paper/2303.15973/) (2023)
- [What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabha](https://mlsanity.com/paper/2304.00235/) (2023)
- [Improving Neural Topic Models with Wasserstein Knowledge Distillation](https://mlsanity.com/paper/2303.15350/) (2023)

## Built on by (most cited first, 4 of 4)

- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [Evaluating Negative Sampling Approaches for Neural Topic Models](https://mlsanity.com/paper/2503.18167/) (2025)
- [HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling](https://mlsanity.com/paper/2505.07157/) (2025)
- [Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation](https://mlsanity.com/paper/2608.16269/) (2026)

## Abstract (arXiv)

Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language models, such as BERT embeddings, into topic modeling. However, they often neglect the intrinsic informational value conveyed by mutual dependencies between words. In this study, we introduce GINopic, a topic modeling framework based on graph isomorphism networks to capture the correlation between words. By conducting intrinsic (quantitative as well as qualitative) and extrinsic evaluations on diverse benchmark datasets, we demonstrate the effectiveness of GINopic compared to existing topic models and highlight its potential for advancing topic modeling.