apache/incubator-hugegraph-ai
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
DGLData IntegrationGraph Neural NetworksHugeGraphMachine LearningPython
Edward Yao
PROFILE
Edward Yao
During November 2024, this developer delivered the HugeGraph-ML End-to-End Graph Learning Platform for the apache/incubator-hugegraph-ai repository. They integrated DGL with HugeGraph to enable comprehensive graph learning workflows, supporting node embedding, node classification, and graph classification tasks. Their work included building data conversion utilities, implementing core model modules, and orchestrating task execution, all in Python. By providing initial examples and tests, they established a foundation for reusable machine learning pipelines on graph data. The depth of the contribution lies in its extensibility, allowing data science and product teams to accelerate experimentation and expand the platform’s capabilities for future development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
3,424
Activity Months1
Your Network
14 people
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 Performance summary for apache/incubator-hugegraph-ai: Delivered the HugeGraph-ML End-to-End Graph Learning Platform, integrating DGL to enable end-to-end graph learning workflows (node embedding, node classification, graph classification). The release includes data conversion utilities, model implementations, task execution orchestration, plus examples and tests, and is backed by commit 08a649efe9e698ef15a5f2dad45eadab61070f6a. This foundation accelerates experimentation, supports reusable ML pipelines on graph data, and expands platform capabilities with demonstrable business value for data science and product teams.
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 Performance summary for apache/incubator-hugegraph-ai: Delivered the HugeGraph-ML End-to-End Graph Learning Platform, integrating DGL to enable end-to-end graph learning workflows (node embedding, node classification, graph classification). The release includes data conversion utilities, model implementations, task execution orchestration, plus examples and tests, and is backed by commit 08a649efe9e698ef15a5f2dad45eadab61070f6a. This foundation accelerates experimentation, supports reusable ML pipelines on graph data, and expands platform capabilities with demonstrable business value for data science and product teams.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
DGLData IntegrationGraph Neural NetworksHugeGraphMachine LearningPython
Repositories Contributed To
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