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Relational Graph Convolution Network

RGCN

22 papers tagged archive 2025-07-28

Introduced by Michael Schlichtkrull et al. in Modeling Relational Data with Graph Convolutional Networks

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

An RGCN, or Relational Graph Convolution Network, is a an application of the GCN framework to modeling relational data, specifically to link prediction and entity classification tasks.

See here for an in-depth explanation of RGCNs by DGL.

PaperSource

Papers archive 2025-07-28

22 shown of 22, 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 54 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
Knowledge Graphs7
Link Prediction5
Relation5
Knowledge Graph Completion3
Node Classification3
General Classification2
Graph Embedding2
Knowledge Graph Embedding2
Multi-Task Learning2
Question Answering2
8k1
C++ code1
Classification1
Continual Learning1
Contrastive Learning1
Decision Making1
Decoder1
Deep Learning1
Diversity1
Drug Discovery1

Usage over time archive 2025-07-28

Papers per year tagged with RGCN: 2017 to 2024, peak 5 5 0 2017: 1 paper 2017 2018: 2 papers 2018 2019: 0 papers 2019 2020: 2 papers 2020 2021: 5 papers 2021 2022: 5 papers 2022 2023: 5 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (22 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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