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Graph Isomorphism Network

GIN

43 papers tagged archive 2025-07-28

Introduced by Keyulu Xu et al. in How Powerful are Graph Neural Networks?

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

Per the authors, Graph Isomorphism Network (GIN) generalizes the WL test and hence achieves maximum discriminative power among GNNs.

PaperSource

Papers archive 2025-07-28

30 shown of 43, 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 59 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 Classification10
Graph Neural Network9
Node Classification7
Graph Learning4
Classification3
General Classification3
Molecular Property Prediction3
Property Prediction3
Adversarial Attack2
Contrastive Learning2
Data Augmentation2
Domain Adaptation2
Feature Engineering2
Graph Property Prediction2
Graph Regression2
Graph Representation Learning2
Link Prediction2
Prediction2
Representation Learning2
Transfer Learning2

Usage over time archive 2025-07-28

Papers per year tagged with GIN: 2018 to 2025, peak 8 8 0 2018: 1 paper 2018 2019: 2 papers 2019 2020: 5 papers 2020 2021: 7 papers 2021 2022: 7 papers 2022 2023: 7 papers 2023 2024: 8 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (43 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

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