mlsanityData to 7 Oct 2026

GINopic: Topic Modeling with Graph Isomorphism Network

arXiv 2404.02115 ↗First posted 2 Apr 2024cs.CL cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

20 of 40 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  6. 2019Topic Modeling in Embedding Spacescited by 224
  7. 2017Autoencoding Variational Inference For Topic Modelscited by 168
  8. 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
  9. 2015Neural Variational Inference for Text Processingcited by 111
  10. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  11. 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 66
  12. 2013Probabilistic Latent Semantic Analysiscited by 63
8 more
  1. 2023Effective Neural Topic Modeling with Embedding Clustering Regularizationcited by 46
  2. 2019Short Text Topic Modeling Techniques, Applications, and Performance: A Surveycited by 38
  3. 2020Neural Topic Modeling by Incorporating Document Relationship Graphcited by 13
  4. 2016Online Nonnegative Matrix Factorization with Outlierscited by 7
  5. 2023Improving Contextualized Topic Models with Negative Samplingcited by 4
  6. 2023Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modelingcited by 3
  7. 2023What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabhacited by 2
  8. 2023Improving Neural Topic Models with Wasserstein Knowledge Distillationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Modelcited by 34
  2. 2025Evaluating Negative Sampling Approaches for Neural Topic Modelscited by 0
  3. 2025HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modelingcited by 0
  4. 2026Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representationcited by 0