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
Developed and delivered the HugeGraph-ML End-to-End Graph Learning Platform for the apache/incubator-hugegraph-ai repository, focusing on integrating DGL with HugeGraph to support comprehensive graph learning workflows. The work included building utilities for data conversion, implementing core model architectures, and orchestrating task execution for node embedding, node classification, and graph classification. Python served as the primary language, leveraging expertise in machine learning and graph neural networks to create reusable pipelines. The platform was validated with initial examples and tests, establishing a robust foundation for future enhancements and enabling data science teams to experiment efficiently with graph-based machine learning solutions.
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
20 peopleShared Repositories
20
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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