Hidden Markov Models for Temporal Graph Representation Learning

We propose the Hidden Markov Model for temporal Graphs, a deep and fully probabilistic model for learning in the domain of dynamic time-varying graphs. We extend hidden Markov models for sequences to the graph domain by stacking probabilistic layers that perform efficient message passing and learn representations for the individual nodes. We evaluate the goodness of the learned representations on temporal node prediction tasks, and we observe promising results compared to neural approaches