Methods › Graphs › Graph Models › CGNN

Crystal Graph Neural Network

CGNN

12 papers tagged archive 2025-07-28

Introduced by Takenori Yamamoto in Crystal Graph Neural Networks for Data Mining in Materials Science

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

The full architecture of CGNN is presented at CGNN's official site.

PaperSource

Papers archive 2025-07-28

12 shown of 12, 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 26 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 Network6
Graph Learning2
Knowledge Graphs2
Management2
Node Classification2
Anomaly Detection1
Autonomous Driving1
Band Gap1
Benchmarking1
Causal Inference1
Classification1
Contrastive Learning1
Deblurring1
Formation Energy1
Image Deblurring1
Materials Screening1
Multivariate Time Series Forecasting1
Question Answering1
Spatio-Temporal Forecasting1
Time Series1

Usage over time archive 2025-07-28

Papers per year tagged with CGNN: 2019 to 2024, peak 4 4 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 2 papers 2021 2022: 2 papers 2022 2023: 3 papers 2023 2024: 4 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (12 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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