daiquocnguyen/QGNN

Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)

37
/ 100
Emerging

This project helps researchers and data scientists analyze complex relationships within knowledge graphs or other network-structured data. By taking your graph data as input, it enhances the way connections are understood, producing more accurate insights for tasks like predicting missing links or classifying nodes and entire graphs. It is designed for those working with relational data and seeking advanced methods to extract deeper meaning.

No commits in the last 6 months.

Use this if you are a researcher or data scientist working with complex networks, like knowledge graphs, and need to improve predictions about relationships or classifications within your data.

Not ideal if you are looking for a simple, off-the-shelf solution for basic tabular data analysis or have no experience with advanced machine learning concepts.

knowledge-graph-analysis network-science relational-data-mining predictive-modeling graph-classification
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 13 / 25

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Stars

51

Forks

7

Language

Python

License

MIT

Last pushed

Dec 25, 2021

Commits (30d)

0

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