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Gated Attention Networks

GaAN

2 papers tagged archive 2025-07-28

Introduced by Jiani Zhang et al. in GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Gated Attention Networks (GaAN) is a new architecture for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head’s importance.

Image credit: GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

PaperSource

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.

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.

TaskPapers
Attribute1
General Classification1
Node Classification1

Usage over time archive 2025-07-28

Papers per year tagged with GaAN: 2018 to 2019, peak 1 1 0 2018: 1 paper 2018 2019: 1 paper 2019
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

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

Graph Models

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