<ul><li>Researchers have addressed the challenge of incorporating document-level metadata into topic modeling to improve topic mixture estimation.</li><li>They propose a graph-structured topic modeling approach that incorporates document-level covariates or known similarities between documents.</li><li>The approach is based on a fast graph-regularized iterative singular value decomposition (SVD) that encourages similar documents to share similar topic mixture proportions.</li><li>Experiments on synthetic datasets and real-world corpora validate the model, showing improved performance and faster inference compared to existing Bayesian methods.</li></ul>

Graph-Structured Topic Modeling for Documents with Spatial or Covariate Dependencies

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