  Understanding the relationships between biomedical terms like viruses, drugs,
and symptoms is essential in the fight against diseases. Many attempts have
been made to introduce the use of machine learning to the scientific process of
hypothesis generation(HG), which refers to the discovery of meaningful implicit
connections between biomedical terms. However, most existing methods fail to
truly capture the temporal dynamics of scientific term relations and also
assume unobserved connections to be irrelevant (i.e., in a positive-negative
(PN) learning setting). To break these limits, we formulate this HG problem as
future connectivity prediction task on a dynamic attributed graph via
positive-unlabeled (PU) learning. Then, the key is to capture the temporal
evolution of node pair (term pair) relations from just the positive and
unlabeled data. We propose a variational inference model to estimate the
positive prior, and incorporate it in the learning of node pair embeddings,
which are then used for link prediction. Experiment results on real-world
biomedical term relationship datasets and case study analyses on a COVID-19
dataset validate the effectiveness of the proposed model.
