alibaba/GraphScope
Mar 2026 – Mar 2026
1 Month active
Languages Used
PythonYAML
Technical Skills
DockerKubernetesdata generationgraph analyticsperformance benchmarking
Lingkai981
PROFILE
Lingkai981
In March 2026, Mingliang Kong developed the Graph Analytics Benchmark Suite for the alibaba/GraphScope repository, focusing on reproducible and cross-platform benchmarking for graph analytics research. He designed and implemented a data generator (FFT-DG), an LLM-based usability evaluation framework, and performance evaluation scripts, all packaged for seamless deployment with Docker and Kubernetes. Using Python and YAML, Mingliang enabled benchmarking across multiple graph platforms, supporting artifact evaluation for SIGMOD 2025. His work addressed the need for standardized, automated performance comparisons, accelerating research-to-production cycles and providing stakeholders with clearer insights into platform capabilities. The month’s efforts centered on robust feature delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,224
Activity Months1
Your Network
36 peopleSame Organization
@sjtu.edu.cn22
ZhongwangZhangMember
Di LinMember
Siyuan FengMember
Zhuoqiang CaiMember
Siyuan FengMember
Xihuai WangMember
StardustMember
李经纬Member
Tiancheng XieMember
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered the Graph Analytics Benchmark Suite for GraphScope to enable reproducible, cross-platform graph analytics benchmarking. The release includes a data generator (FFT-DG), an LLM-based usability evaluation framework, and performance evaluation scripts across multiple graph platforms, packaged for Kubernetes + Docker. This work supports the SIGMOD 2025 artifacts and enhances benchmarking capabilities, accelerating research-to-production cycles and enabling clearer performance comparisons for stakeholders.
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered the Graph Analytics Benchmark Suite for GraphScope to enable reproducible, cross-platform graph analytics benchmarking. The release includes a data generator (FFT-DG), an LLM-based usability evaluation framework, and performance evaluation scripts across multiple graph platforms, packaged for Kubernetes + Docker. This work supports the SIGMOD 2025 artifacts and enhances benchmarking capabilities, accelerating research-to-production cycles and enabling clearer performance comparisons for stakeholders.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
DockerKubernetesdata generationgraph analyticsperformance benchmarking
Repositories Contributed To
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