# Neural Topic Modeling by Incorporating Document Relationship Graph

arXiv: https://arxiv.org/abs/2009.13972 · HTML: https://mlsanity.com/paper/2009.13972/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023)
- [Neural Attention-Aware Hierarchical Topic Model](https://mlsanity.com/paper/2110.07161/) (2021)
- [GINopic: Topic Modeling with Graph Isomorphism Network](https://mlsanity.com/paper/2404.02115/) (2024)
- [Importance-aware Topic Modeling for Discovering Public Transit Risk from Noisy Social Media](https://mlsanity.com/paper/2512.06293/) (2025)
- [Graph Contrastive Topic Model](https://mlsanity.com/paper/2307.02078/) (2023)
- [Textual Analysis of ICALEPCS and IPAC Conference Proceedings: Revealing Research Trends, Topics, and Collaborations for Future Insights and Advanced Search](https://mlsanity.com/paper/2310.08954/) (2023)
- [Anchor Prediction: A Topic Modeling Approach](https://mlsanity.com/paper/2205.14631/) (2022)
- [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)
- [A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes](https://mlsanity.com/paper/2609.29630/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community. In this paper, we propose Graph Topic Model (GTM), a GNN based neural topic model that represents a corpus as a document relationship graph. Documents and words in the corpus become nodes in the graph and are connected based on document-word co-occurrences. By introducing the graph structure, the relationships between documents are established through their shared words and thus the topical representation of a document is enriched by aggregating information from its neighboring nodes using graph convolution. Extensive experiments on three datasets were conducted and the results demonstrate the effectiveness of the proposed approach.