awslabs/graphstorm
Mar 2025 – Jul 2025
3 Months active
Languages Used
C++PythonShell
Technical Skills
DGLEnd-to-End TestingFeature EngineeringGraph Neural NetworksMachine LearningModel Development
Oxfordblue7
PROFILE
Oxfordblue7
Han Xie contributed to the awslabs/graphstorm repository by developing three core features over three months, focusing on enhancing model evaluation and training workflows for graph neural networks. He implemented RGAT edge feature support, introducing new modules and end-to-end tests to validate edge-feature configurations across multiple tasks. Han also expanded binary classification capabilities by adding precision_at_recall and recall_at_precision metrics, updating evaluation functions, configuration, and documentation. Additionally, he enabled fixed-test-size configuration for link prediction training, improving reproducibility and benchmarking. His work demonstrated depth in Python, PyTorch, and configuration management, with a strong emphasis on robust testing and reliable model assessment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
3,334
Activity Months3
Your Network
1388 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Implemented a fixed-test-size configuration for Link Prediction (LP) training in the LM framework within awslabs/graphstorm. This feature passes the fixed-test-size parameter to dataloader classes to enable controlled evaluation, accompanied by a dedicated test case verifying functionality with a specific dataset and configuration. These changes improve evaluation reproducibility, benchmarking consistency, and overall model selection reliability.
1 Commits • 1 Features
Jul 1, 2025July 2025: Implemented a fixed-test-size configuration for Link Prediction (LP) training in the LM framework within awslabs/graphstorm. This feature passes the fixed-test-size parameter to dataloader classes to enable controlled evaluation, accompanied by a dedicated test case verifying functionality with a specific dataset and configuration. These changes improve evaluation reproducibility, benchmarking consistency, and overall model selection reliability.
July 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for awslabs/graphstorm. Delivered two new binary classification evaluation metrics: precision_at_recall and recall_at_precision, expanding model assessment capabilities. Changes include updates to evaluation functions, configuration, documentation, and new tests to validate functionality. Core commit: 76b2f517229a7c0e3019d1c79a2878613f1b5b33. No major bugs reported this month. Impact: enhanced evaluation accuracy for binary tasks, enabling better model selection and reliability. Technologies/skills demonstrated: Python, ML metric design, testing, documentation, and configuration management.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for awslabs/graphstorm. Delivered two new binary classification evaluation metrics: precision_at_recall and recall_at_precision, expanding model assessment capabilities. Changes include updates to evaluation functions, configuration, documentation, and new tests to validate functionality. Core commit: 76b2f517229a7c0e3019d1c79a2878613f1b5b33. No major bugs reported this month. Impact: enhanced evaluation accuracy for binary tasks, enabling better model selection and reliability. Technologies/skills demonstrated: Python, ML metric design, testing, documentation, and configuration management.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for awslabs/graphstorm focused on delivering RGAT edge features and strengthening test coverage. No major bugs reported this month; emphasis on feature delivery, reliability, and cross-task validation.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for awslabs/graphstorm focused on delivering RGAT edge features and strengthening test coverage. No major bugs reported this month; emphasis on feature delivery, reliability, and cross-task validation.
March 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability92.6%
Architecture92.6%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
Configuration ManagementDGLData ScienceEnd-to-End TestingFeature EngineeringGraph Neural NetworksLink PredictionMachine LearningModel DevelopmentModel EvaluationPyTorchPython ScriptingShell ScriptingSoftware DevelopmentTesting
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