Papers › GINopic: Topic Modeling with Graph Isomorphism Network

GINopic: Topic Modeling with Graph Isomorphism Network

2 Apr 2024arXiv:2404.02115archive 2025-07-28

Suman Adhya, Debarshi Kumar Sanyal

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.

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