Graph Models
Recurrent Event Network
Introduced by Jin et al. in Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge GraphsRecurrent 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
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Knowledge Graphs | 2 | 33.33% |
| Link Prediction | 2 | 33.33% |
| Temporal Sequences | 2 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
