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Graph Convolutional Network

GCN

968 papers tagged archive 2025-07-28

Introduced by Thomas N. Kipf et al. in Semi-Supervised Classification with Graph Convolutional Networks

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. The choice of convolutional architecture is motivated via a localized first-order approximation of spectral graph convolutions. The model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes.

PaperSource

Papers archive 2025-07-28

30 shown of 968, 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 490 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 Classification157
Graph Neural Network81
Representation Learning71
Graph Learning68
Classification67
General Classification60
Action Recognition48
Graph Classification47
Link Prediction44
Skeleton Based Action Recognition43
Recommendation Systems39
Graph Embedding32
Graph Attention31
Prediction31
Clustering29
Knowledge Graphs27
Collaborative Filtering25
Graph Representation Learning25
Pose Estimation25
Relation24

Usage over time archive 2025-07-28

Papers per year tagged with GCN: 2016 to 2025, peak 203 203 0 2016: 1 paper 2016 2017: 5 papers 2017 2018: 28 papers 2018 2019: 114 papers 2019 2020: 159 papers 2020 2021: 203 papers 2021 2022: 155 papers 2022 2023: 125 papers 2023 2024: 139 papers 2024 2025: 39 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (968 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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