Learning temporal attention in dynamic graphs with bilinear interactions
Fig 3
Overview of our approach relative to DyRep [14], in the context of dynamic link prediction.
During training, events ot are observed, affecting node embeddings Z. In contrast to DyRep, which updates attention weights St in a predefined hard-coded way based on associative connections At, such as CloseFriend, we assume that graph At is unknown and our latent dynamic graph (LDG) model based on NRI [15] infers St by observing how nodes communicate. We show that learned St has a close relationship to certain associative connections. Best viewed in colour.