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Dynamic Topic Modeling with a Higher-Order Hypergraphical Representation

arXiv 2605.28269 ↗First posted 27 May 2026cs.LG stat.MEcited by 0

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Abstract (arXiv)

Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models represent each topic through a single probability vector on the multinomial simplex and implicitly couple word occurrence and repetition within one probabilistic mechanism. However, this formulation restricts the dependence structure among words and overlooks informative higher-order interactions, particularly in dynamic corpora with overlapping semantics. To address these limitations, we introduce a hypergraph representation of text where each document is modeled as a hyperedge connecting all co-occurring words, with repetition intensities encoded as node weights. This representation naturally separates word occurrence from repetition and induces a novel hypergraph-based multinomial distribution with a nonlinear normalization depending on the observed word set of each document. Building on this likelihood, we develop a dynamic topic modeling framework via structured low-rank factorizations with explicit temporal regularization on topic-word profiles. Moreover, we establish local convergence guarantees and derive non-asymptotic error bounds despite the intrinsic nonconvexity induced by bilinear factorization and document-specific nonlinear normalization. Numerical experiments on synthetic data and an application to the International Conference on Learning Representations (ICLR) corpus demonstrate consistent improvements over existing multinomial-based topic models.

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References · 16

  1. 2018Graph Convolutional Networks for Text Classificationcited by 345
  2. 2017Autoencoding Variational Inference For Topic Modelscited by 169
  3. 2018Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overviewcited by 160
  4. 2020Be More with Less: Hypergraph Attention Networks for Inductive Text Classificationcited by 74
  5. 2015Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guaranteescited by 70
  6. 2013Probabilistic Latent Semantic Analysiscited by 63
  7. 2019Community Detection for Hypergraph Networks via Regularized Tensor Power Iterationcited by 27
  8. 2016The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differencescited by 15
  9. 2017Using SVD for Topic Modelingcited by 14
  10. 2024HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphscited by 7
  11. 2024Learning representations of learning representationscited by 5
  12. 2024A General Latent Embedding Approach for Modeling Non-uniform High-dimensional Sparse Hypergraphs with Multiplicitycited by 4
4 more
  1. 2023Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networkscited by 3
  2. 2023Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notescited by 1
  3. 2024Multi-environment Topic Modelscited by 1
  4. 2025Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guaranteescited by 1

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