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 GraphsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Knowledge Graphs | 2 | 33.33% |
Link Prediction | 2 | 33.33% |
Temporal Sequences | 2 | 33.33% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |