Dynamic Topic Models for Temporal Document Networks
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26281-26292, 2022.
Dynamic topic models explore the time evolution of topics in temporally accumulative corpora. While existing topic models focus on the dynamics of individual documents, we propose two neural topic models aimed at learning unified topic distributions that incorporate both document dynamics and network structure. For the first model, by adding a time dimension, we propose Time-Aware Optimal Transport, which measures the probability of a link between two differently timestamped documents using their semantic distance. Since the gradually evolving topological structure of network may also influence the establishment of a new link, for the second model, we further design a Temporal Point Process to capture the impact of historical neighbors on the current link formation at the network level. Experiments on four dynamic document networks demonstrate the advantage of our models in jointly modeling document dynamics and network adjacency.