neo4j/graph-data-science-client
Nov 2024 – Oct 2025
4 Months active
Languages Used
PythonRST
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentDocumentation
ioannispan
PROFILE
Ioannispan
Ioannis Panagiotas developed and enhanced resource estimation and documentation features for the neo4j/graph-data-science-client repository over four months. He focused on designing and implementing API endpoints that estimate memory consumption for advanced graph algorithms, including prize collector Steiner tree, harmonic centrality, and max flow operations. Using Python and RST, Ioannis delivered endpoints such as memory.list and memory.summary, enabling users to proactively plan resource allocation and avoid out-of-memory errors. His work included comprehensive API documentation, refactoring for maintainability, and alignment with repository structure, resulting in improved usability, onboarding, and integration for developers working with complex graph data science workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
5
Lines of code
257
Activity Months4
Your Network
33 peopleSame Organization
@neotechnology.com15
Anton KlarénMember
Bledi FeshtiMember
Brian ShiMember
Neo Technology Build AgentMember
Hannes SandbergMember
Henrik NymanMember
Lars KordunerMember
Love LeiflandMember
Nacho CordonMember
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for neo4j/graph-data-science-client focusing on the addition of resource estimation capabilities for max flow operations. Delivered memory consumption estimation endpoints to help users plan resources before executing computationally intensive algorithms (mutate, stats, stream, write). The work includes establishing a dedicated API surface, improving predictability for large-scale graph analytics, and laying groundwork for future estimation-based capacity planning and cost insights.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for neo4j/graph-data-science-client focusing on the addition of resource estimation capabilities for max flow operations. Delivered memory consumption estimation endpoints to help users plan resources before executing computationally intensive algorithms (mutate, stats, stream, write). The work includes establishing a dedicated API surface, improving predictability for large-scale graph analytics, and laying groundwork for future estimation-based capacity planning and cost insights.
October 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for neo4j/graph-data-science-client. Delivered Harmonic Centrality Estimation Endpoints for Resource Planning with memory consumption estimates, enabling proactive capacity planning and resource allocation for graph processing workflows. No major bugs fixed this month.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for neo4j/graph-data-science-client. Delivered Harmonic Centrality Estimation Endpoints for Resource Planning with memory consumption estimates, enabling proactive capacity planning and resource allocation for graph processing workflows. No major bugs fixed this month.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024: Delivered comprehensive API documentation for the latest Graph Data Science client endpoints in the neo4j/graph-data-science-client repository. Focused on documenting new endpoints across pcst (prize collector Steiner tree) including mutate, mutate.estimate, stats, stats.estimate, write, and write.estimate; plus documentation for gds.alpha.ml.splitRelationships.mutate.estimate and for HashGNN write/estimate endpoints. This work improves developer onboarding, accelerates integration, and enhances API discoverability and correctness across the product surface.
3 Commits • 1 Features
Dec 1, 2024December 2024: Delivered comprehensive API documentation for the latest Graph Data Science client endpoints in the neo4j/graph-data-science-client repository. Focused on documenting new endpoints across pcst (prize collector Steiner tree) including mutate, mutate.estimate, stats, stats.estimate, write, and write.estimate; plus documentation for gds.alpha.ml.splitRelationships.mutate.estimate and for HashGNN write/estimate endpoints. This work improves developer onboarding, accelerates integration, and enhances API discoverability and correctness across the product surface.
December 2024
November 2024
7 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11 focusing on the neo4j/graph-data-science-client repository. The work centered on documentation improvements for Steiner tree functionality and a targeted API refactor to strengthen memory-related endpoints, aligning with the product's usability and maintainability goals.
November 2024
7 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11 focusing on the neo4j/graph-data-science-client repository. The work centered on documentation improvements for Steiner tree functionality and a targeted API refactor to strengthen memory-related endpoints, aligning with the product's usability and maintainability goals.
Activity
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Quality Metrics
Correctness98.4%
Maintainability98.4%
Architecture98.4%
Performance96.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRST
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentDocumentation
Repositories Contributed To
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