Hyperbolic Graph Topic Modeling Network with Continuously Updated Topic Tree

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a Hyperbolic Graph Topic Modeling Network to integrate both network hierarchy across linked documents and semantic hierarchy within texts into a unified HGNN framework. Specifically, we construct a two-layer document graph. Intra- and cross-layer encoding captures network hierarchy. We design a topic tree for text decoding to preserve semantic hierarchy and learn interpretable topics. Supervised and unsupervised experiments verify the effectiveness of our model.