pyg-team/pytorch_geometric
Apr 2025 – Apr 2025
1 Month active
Languages Used
C++Python
Technical Skills
Data TransformationDeep LearningGraph Neural NetworksModel ImplementationPre-trained ModelsPyTorch
Semih Cantürk
PROFILE
Semih Cantürk
Semih Canturk integrated the Graph Positional and Structural Encoder (GPSE) into the pyg-team/pytorch_geometric repository, expanding the library’s graph representation capabilities. He developed the main GPSE module along with supporting encoding and processing helpers, a pre-trained model loader, and a transform for attaching GPSE encodings to graph data. Using Python and PyTorch, Semih ensured the workflow could seamlessly enrich graph neural network pipelines with positional and structural information. Comprehensive test modules and loss functions were included to validate reliability and compatibility with existing components. This work demonstrated depth in model implementation, data transformation, and deep learning within the PyG ecosystem.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,239
Activity Months1
Your Network
46 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pyg-team/pytorch_geometric: Focused on expanding graph representation capabilities by integrating the Graph Positional and Structural Encoder (GPSE). Delivered a complete GPSE integration, including the main GPSE module, encoding/processing helpers, a pre-trained model loader, a transform to attach GPSE encodings to graphs, and corresponding test modules and loss functions. This work enables users to enrich graph representations with GPSE encodings and streamlines adoption in downstream GNN pipelines.
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pyg-team/pytorch_geometric: Focused on expanding graph representation capabilities by integrating the Graph Positional and Structural Encoder (GPSE). Delivered a complete GPSE integration, including the main GPSE module, encoding/processing helpers, a pre-trained model loader, a transform to attach GPSE encodings to graphs, and corresponding test modules and loss functions. This work enables users to enrich graph representations with GPSE encodings and streamlines adoption in downstream GNN pipelines.
April 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Data TransformationDeep LearningGraph Neural NetworksModel ImplementationPre-trained ModelsPyTorch
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