awslabs/graphstorm
Nov 2025 – Dec 2025
2 Months active
Languages Used
Python
Technical Skills
Deep LearningGraph Neural NetworksMachine LearningPyTorchPythondata processing
Haiyang Yu
PROFILE
Haiyang Yu
Worked on the awslabs/graphstorm repository to enhance graph neural network models by adding edge feature support for GAT and SAGE architectures on homogeneous graphs. Developed edge-aware pathways in GATConv and SAGEConvWithEdgeFeat, updating encoders and integrating edge attributes to improve model expressiveness. Introduced a Mitra embeddings generator tool to automate node feature engineering, streamlining experimentation for GNN tasks. Emphasized robust test-driven development by expanding unit test coverage for new features and integration points. Leveraged Python and PyTorch throughout, focusing on deep learning, data processing, and feature engineering to enable richer graph representations and more flexible modeling for downstream machine learning applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
3,006
Activity Months2
Your Network
7 peopleWork History
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for awslabs/graphstorm: - Key features delivered include edge features support for the SAGE model on homogeneous graphs (edge_feat usage added in gsf.py and sage_encoder.py), introducing a new SAGEConvWithEdgeFeat pathway, and a Mitra embeddings generator tool to automate feature engineering for GNN tasks. - Major tests updates accompany the feature work with added test coverage in test_nn_encoder.py and test_nn_model.py to validate edge-feature handling and Mitra integration. - The work is exemplified by two commits that implement these changes: f3a063669aba254889d4902f6f5c7d512401632e (Add edge features support for homogenous GNN: SAGE (#1353)) and 712b80327741aa8c04238f027cba356d4f4e9c18 (Add mitra embeddings support example (#1352)). - No separate critical bug fixes are reported this month; the focus was on feature delivery and test coverage. - Business value: richer graph representations via edge features enable improved modeling for homogeneous graphs, while the Mitra embeddings tool accelerates feature engineering and experimentation, reducing manual effort and time to value. This enhances model performance opportunities for downstream tasks and demonstrates practical end-to-end tooling in GNN workflows. - Technologies/skills demonstrated: graph neural networks (SAGE), edge features integration, Mitra embeddings, PyTorch-based GNN tooling, encoder/feature engineering pipelines (gsf.py, sage_encoder.py), automated feature generation, and test-driven development.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for awslabs/graphstorm: - Key features delivered include edge features support for the SAGE model on homogeneous graphs (edge_feat usage added in gsf.py and sage_encoder.py), introducing a new SAGEConvWithEdgeFeat pathway, and a Mitra embeddings generator tool to automate feature engineering for GNN tasks. - Major tests updates accompany the feature work with added test coverage in test_nn_encoder.py and test_nn_model.py to validate edge-feature handling and Mitra integration. - The work is exemplified by two commits that implement these changes: f3a063669aba254889d4902f6f5c7d512401632e (Add edge features support for homogenous GNN: SAGE (#1353)) and 712b80327741aa8c04238f027cba356d4f4e9c18 (Add mitra embeddings support example (#1352)). - No separate critical bug fixes are reported this month; the focus was on feature delivery and test coverage. - Business value: richer graph representations via edge features enable improved modeling for homogeneous graphs, while the Mitra embeddings tool accelerates feature engineering and experimentation, reducing manual effort and time to value. This enhances model performance opportunities for downstream tasks and demonstrates practical end-to-end tooling in GNN workflows. - Technologies/skills demonstrated: graph neural networks (SAGE), edge features integration, Mitra embeddings, PyTorch-based GNN tooling, encoder/feature engineering pipelines (gsf.py, sage_encoder.py), automated feature generation, and test-driven development.
December 2025
November 2025
2 Commits • 1 Features
Nov 1, 20252025-11 Monthly Summary for awslabs/graphstorm focusing on edge feature support for GAT models on homogeneous graphs. Implemented edge feature support to enable edge-aware GATConv, updated GATEncoder and GATV2 encoder, and added unit tests to validate edge_feat integration. No major bugs fixed this period; primary emphasis on feature delivery and test coverage.
November 2025
2 Commits • 1 Features
Nov 1, 20252025-11 Monthly Summary for awslabs/graphstorm focusing on edge feature support for GAT models on homogeneous graphs. Implemented edge feature support to enable edge-aware GATConv, updated GATEncoder and GATV2 encoder, and added unit tests to validate edge_feat integration. No major bugs fixed this period; primary emphasis on feature delivery and test coverage.
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture95.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningGraph Neural NetworksMachine LearningPyTorchPythondata processingfeature engineeringgraph neural networksmachine learning
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