Methods › Graphs › Graph Models › Graph Convolutional Networks

Graph Convolutional Networks

388 papers tagged archive 2025-07-28

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

A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs.

Image source: Semi-Supervised Classification with Graph Convolutional Networks

Source: Semi-Supervised Classification with Graph Convolutional Networks

Papers archive 2025-07-28

30 shown of 388, 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

20 shown of 325 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
Node Classification63
General Classification50
Classification33
Representation Learning32
Action Recognition25
Link Prediction21
Skeleton Based Action Recognition21
Graph Embedding20
Clustering19
Graph Attention19
Graph Classification19
Recommendation Systems19
Knowledge Graphs18
Prediction18
Sentence17
Graph Learning15
Graph Neural Network13
Attribute12
Graph Representation Learning12
Relation12

Usage over time archive 2025-07-28

Papers per year tagged with Graph Convolutional Networks: 2016 to 2021, peak 137 137 0 2016: 1 paper 2016 2017: 1 paper 2017 2018: 33 papers 2018 2019: 111 papers 2019 2020: 137 papers 2020 2021: 105 papers 2021
Papers per year the archive tags with this method, by the paper's archive date (388 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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