Our model is similar in spirit to topic models: for an input dataset, the output of the RMN is a set of relationship descriptors (topics) and-for each relationship in the dataset-a trajectory, or a sequence of probability distributions over these descriptors (document-topic assignments).
the RMN uses recent advances in deep learning to achieve better control over descriptor coherence and trajectory smoothness (Section 4).