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Graph sampling based inductive learning method

GraphSAINT

7 papers tagged archive 2025-07-28

Introduced by Hanqing Zeng et al. in GraphSAINT: Graph Sampling Based Inductive Learning Method

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

Scalable method to train large scale GNN models via sampling small subgraphs.

PaperSource

Papers archive 2025-07-28

7 shown of 7, 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

15 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 Classification4
Graph Learning2
Node Property Prediction2
Attribute1
Deep Learning1
Graph Attention1
Graph Embedding1
Graph Neural Network1
Graph Representation Learning1
Graph Sampling1
Inductive Learning1
Link Prediction1
Reinforcement Learning (RL)1
graph partitioning1
whole slide images1

Usage over time archive 2025-07-28

Papers per year tagged with GraphSAINT: 2019 to 2024, peak 2 2 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 2 papers 2022 2023: 2 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (7 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 Representation Learning

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