alibaba/GraphScope
Jan 2025 – Jan 2025
1 Month active
Languages Used
C++
Technical Skills
Algorithm ImplementationCommunity DetectionGraph AlgorithmsSoftware Engineering
Songqing Zhang
PROFILE
Songqing Zhang
During January 2025, Songqing Zhang enhanced the Louvain clustering implementation in the alibaba/GraphScope repository, focusing on algorithm correctness and reliability. Working in C++ and applying expertise in graph algorithms and community detection, Zhang addressed a critical bug by refining the stopping condition to halt when clustering quality began to decrease, aligning the behavior with NetworkX standards. The quality metric calculation was also corrected to prevent double-counting edge weights in undirected graphs, and an assertion was added to ensure computed quality did not exceed 1.0. These targeted improvements increased the accuracy and interoperability of the Louvain algorithm within GraphScope.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
0
Lines of code
17
Activity Months1
Your Network
14 peopleWork History
January 2025
2 Commits
Jan 1, 2025January 2025 performance summary for alibaba/GraphScope: Delivered critical correctness improvements to the Louvain clustering implementation. The work focused on stopping condition alignment with NetworkX and accurate quality computation, including edge-weight handling for undirected graphs and a validation assertion to cap quality at 1.0. These changes reduce the risk of incorrect clustering decisions and improve overall reliability and interoperability with standard graph libraries.
2 Commits
Jan 1, 2025January 2025 performance summary for alibaba/GraphScope: Delivered critical correctness improvements to the Louvain clustering implementation. The work focused on stopping condition alignment with NetworkX and accurate quality computation, including edge-weight handling for undirected graphs and a validation assertion to cap quality at 1.0. These changes reduce the risk of incorrect clustering decisions and improve overall reliability and interoperability with standard graph libraries.
January 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Algorithm ImplementationCommunity DetectionGraph AlgorithmsSoftware Engineering
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