  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.
