Methods › Graphs › Graph Models › RE-NET
Recurrent Event Network
RE-NET
Introduced by Woojeong Jin et al. in Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs 1 Nov 2020 · 0 repositories
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Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs 11 Apr 2019 · 2 repositories · arXiv:1904.05530
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Knowledge Graphs | 2 |
| Link Prediction | 2 |
| Temporal Sequences | 2 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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