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Gated Graph Sequence Neural Networks

GGS-NNs

9 papers tagged archive 2025-07-28

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

Gated Graph Sequence Neural Networks (GGS-NNs) is a novel graph-based neural network model. GGS-NNs modifies Graph Neural Networks (Scarselli et al., 2009) to use gated recurrent units and modern optimization techniques and then extend to output sequences.

Source: Li et al.

Image source: Li et al.

Source: Gated Graph Sequence Neural Networks

Papers archive 2025-07-28

9 shown of 9, 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

16 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
Graph Neural Network3
Prediction2
CPU1
Clustering1
Drug Discovery1
Graph Classification1
Management1
Method name prediction1
Node Classification1
Object1
Parameter Prediction1
Question Generation1
Question-Generation1
Relationship Detection1
SQL-to-Text1
Visual Relationship Detection1

Usage over time archive 2025-07-28

Papers per year tagged with GGS-NNs: 2015 to 2021, peak 3 3 0 2015: 1 paper 2015 2016: 0 papers 2016 2017: 1 paper 2017 2018: 0 papers 2018 2019: 2 papers 2019 2020: 3 papers 2020 2021: 2 papers 2021
Papers per year the archive tags with this method, by the paper's archive date (9 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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