Methods › Graphs › Graph Models › AGCN

Adaptive Graph Convolutional Neural Networks

AGCN

8 papers tagged archive 2025-07-28

Introduced by Ruoyu Li et al. in Adaptive Graph Convolutional Neural Networks

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

AGCN is a novel spectral graph convolution network that feed on original data of diverse graph structures.

Image credit: Adaptive Graph Convolutional Neural Networks

PaperSource

Papers archive 2025-07-28

8 shown of 8, 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

17 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
Attribute2
Clustering1
Decoder1
Deep Clustering1
Event data classification1
General Classification1
Graph Clustering1
Graph Embedding1
Graph Representation Learning1
Metric Learning1
Multi-Label Image Recognition1
Recommendation Systems1
Representation Learning1
Scene Classification1
Scene Recognition1
Scene Understanding1
Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with AGCN: 2018 to 2023, peak 2 2 0 2018: 1 paper 2018 2019: 2 papers 2019 2020: 1 paper 2020 2021: 1 paper 2021 2022: 2 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (8 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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