Topic modeling on document networks with Dirichlet Optimal Transport Barycenter (Extended Abstract)

• Main Authors: ZHANG, Ce, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Graph Neural Networks; Text Mining; Optimal Transport; Dirichlet Distribution; Document Networks; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9840; https://ink.library.smu.edu.sg/context/sis_research/article/10840/viewcontent/icde24.pdf
• Institution: Singapore Management University

Texts are often interconnected in a network structure, e.g., academic papers via citations. On the one hand, though Graph Neural Networks (GNNs) have shown promising ability to derive effective embeddings for networked documents, they do not assume latent topics, resulting in uninterpretahle embeddings. On the other hand, topic models can infer interpretable document representations. However, most topic models focus on plain text and fail to leverage network structure across documents. In this paper, we propose a GNN-based topic model that both captures network connection and derives semantically interpretable text representations. For network modeling, we build our model with Optimal Transport Barycenter. For semantic interpretability, we extend optimal transport with pre-trained word embeddings.