RE-NET
Recurrent Event Network
Recurrent Event Network (RE-NET) is an autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. RE-NET employs a recurrent event encoder to encode past facts and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules.
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
https://paperswithcode.com/paper/recurrent-event-network-for-reasoning-over
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs