neo4j/graph-data-science-client
Nov 2024 – Dec 2025
5 Months active
Languages Used
PythonRST
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentDocumentationAPI developmentdata science
ioannispan
PROFILE
Ioannispan
Worked on the neo4j/graph-data-science-client repository, delivering six new features over five months focused on expanding and refining API endpoints for advanced graph algorithms. Developed and documented estimation endpoints for resource planning, including memory usage for harmonic centrality, max flow, and minimum cost flow operations, enabling users to proactively allocate resources and optimize large-scale analytics workflows. Enhanced API consistency and usability through targeted refactors and comprehensive documentation updates, supporting faster developer onboarding and integration. Leveraged Python and RST for backend development and documentation, applying expertise in API design, data science, and graph algorithms to improve maintainability and product coverage.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
6
Lines of code
353
Activity Months5
Your Network
35 peopleSame Organization
@neotechnology.com17
Anton KlarénMember
Bledi FeshtiMember
Brian ShiMember
Neo Technology Build AgentMember
Hannes SandbergMember
Henrik NymanMember
Lars KordunerMember
Love LeiflandMember
Nacho CordonMember
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 — Key feature delivery and API expansion for the graph data science client. Delivered new endpoints to estimate maximum flow at minimum cost (MCMF) in graph algorithms, enabling cost-efficient flow computations for users. Implemented and wired endpoints in neo4j/graph-data-science-client with commit 96392494354f261f71ec14278ff52d0aa0c59e83. This expansion broadens API coverage for graph optimization tasks, supporting more accurate modeling of network flows and cost trade-offs. No major bugs reported this month; primary focus was feature delivery with clear business value: faster insight into optimization problems and potential cost savings for large-scale networks. Technologies demonstrated include graph algorithms, REST API design, and end-to-end backend integration with traceable commits.
1 Commits • 1 Features
Dec 1, 2025December 2025 — Key feature delivery and API expansion for the graph data science client. Delivered new endpoints to estimate maximum flow at minimum cost (MCMF) in graph algorithms, enabling cost-efficient flow computations for users. Implemented and wired endpoints in neo4j/graph-data-science-client with commit 96392494354f261f71ec14278ff52d0aa0c59e83. This expansion broadens API coverage for graph optimization tasks, supporting more accurate modeling of network flows and cost trade-offs. No major bugs reported this month; primary focus was feature delivery with clear business value: faster insight into optimization problems and potential cost savings for large-scale networks. Technologies demonstrated include graph algorithms, REST API design, and end-to-end backend integration with traceable commits.
December 2025
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.
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.
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.
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
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.
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.
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.
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
Activity
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Quality Metrics
Correctness98.4%
Maintainability97.0%
Architecture98.4%
Performance95.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRST
Technical Skills
API DesignAPI DevelopmentAPI developmentBackend DevelopmentDocumentationdata sciencegraph algorithms
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