pyg-team/pytorch_geometric
May 2025 – May 2025
1 Month active
Languages Used
JinjaPython
Technical Skills
Data EngineeringDeep LearningGraph Neural NetworksMachine LearningPyTorch
Blaž Stojanovič
PROFILE
Blaž Stojanovič
Blaz Stojanovic developed a Relational Deep Learning example for the pyg-team/pytorch_geometric repository, enabling researchers to prototype relational graph neural networks on the RelBench dataset. The solution featured an rdl.py script that demonstrated training with heterogeneous graph encoders, temporal encoders, and a GraphSAGE model, leveraging Python and PyTorch to address complex relational data scenarios. Blaz also updated project documentation, including the CHANGELOG and example guides, to improve onboarding and discoverability. This work expanded the repository’s model capabilities and reduced time-to-prototype for users, reflecting a strong grasp of data engineering and deep learning within a collaborative open-source context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
735
Activity Months1
Your Network
32 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pyg-team/pytorch_geometric: Delivered a Relational Deep Learning (RDL) example that enables researchers to prototype relational GNNs on the RelBench dataset. The example includes an rdl.py script demonstrating training with heterogeneous graph encoders, temporal encoders, and a GraphSAGE model. Documentation updates were made to CHANGELOG.md and examples/README.md to reflect the new example, improving onboarding and discoverability. This work enhances business value by expanding model capabilities and reducing time-to-prototype for relational data scenarios, while showcasing strong code quality and collaboration signals.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pyg-team/pytorch_geometric: Delivered a Relational Deep Learning (RDL) example that enables researchers to prototype relational GNNs on the RelBench dataset. The example includes an rdl.py script demonstrating training with heterogeneous graph encoders, temporal encoders, and a GraphSAGE model. Documentation updates were made to CHANGELOG.md and examples/README.md to reflect the new example, improving onboarding and discoverability. This work enhances business value by expanding model capabilities and reducing time-to-prototype for relational data scenarios, while showcasing strong code quality and collaboration signals.
May 2025
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaPython
Technical Skills
Data EngineeringDeep LearningGraph Neural NetworksMachine LearningPyTorch
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