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
Developed and integrated the Graph Positional and Structural Encoder (GPSE) into the pyg-team/pytorch_geometric repository, expanding the library’s graph representation capabilities. The work involved implementing the main GPSE module, supporting encoding and processing helpers, a loader for pre-trained models, and a transform to attach GPSE encodings to graph data within PyTorch Geometric pipelines. Comprehensive test modules and loss functions were included to ensure reliability and compatibility with existing components. Leveraging Python, C++, and PyTorch, this contribution enables users to enrich graph neural network workflows with advanced positional and structural encodings, streamlining adoption of GPSE in downstream machine learning tasks.
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
51 peopleSame Organization
@umontreal.ca16
Christian RémillardMember
David ValentineMember
Emile MichaudMember
emirgcMember
Éric DupuisMember
Eve CharbonneauMember
JozefRivestMember
JuliePecMember
Laurie-AnneMember
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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