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GraphSAGE

132 papers tagged archive 2025-07-28

Introduced by William L. Hamilton et al. in Inductive Representation Learning on Large Graphs

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

GraphSAGE is a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data.

Image from: Inductive Representation Learning on Large Graphs

PaperSource

Papers archive 2025-07-28

30 shown of 132, 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 124 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 Network40
Node Classification24
Representation Learning23
Graph Attention14
GPU11
Link Prediction11
Graph Classification10
Graph Representation Learning9
General Classification8
Graph Learning7
Recommendation Systems7
Classification6
Transfer Learning6
CPU5
Fraud Detection5
Intrusion Detection5
Benchmarking4
Contrastive Learning4
Decision Making4
Deep Learning4

Usage over time archive 2025-07-28

Papers per year tagged with GraphSAGE: 2017 to 2025, peak 25 25 0 2017: 1 paper 2017 2018: 3 papers 2018 2019: 11 papers 2019 2020: 9 papers 2020 2021: 22 papers 2021 2022: 21 papers 2022 2023: 24 papers 2023 2024: 25 papers 2024 2025: 16 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (132 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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