Graph Models

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.

Source: Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs


Paper Code Results Date Stars


Task Papers Share
Knowledge Graphs 2 33.33%
Link Prediction 2 33.33%
Temporal Sequences 2 33.33%


Component Type
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