Cross-Graph Transitions for Graph Database Queries

The specialized query operator for graph databases allows seamless transitions between graphs, enabling efficient and coherent data retrieval with confidence-based filtering and validation, addressing inefficiencies and errors in traditional graph query systems.

WO2026143318A1PCT designated stage Publication Date: 2026-07-09PAYPAL INC
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
PAYPAL INC
Filing Date
2024-12-30
Publication Date
2026-07-09

AI Technical Summary

Technical Problem

Traditional graph query operations are confined to a single graph and lack native support for seamless transitions between multiple graphs, leading to inefficiencies and errors, especially when dealing with large and complex datasets, and do not facilitate real-time data validation or confidence-based filtering.

Method used

A specialized query operator enables seamless transitions between graphs within a single traversal context, incorporating confidence scores to filter and prioritize results, and uses a mirror graph for data validation to ensure integrity.

Benefits of technology

This approach reduces computational overhead, ensures efficient and coherent data retrieval across multiple graphs, enhances data analysis with nuanced filtering, and maintains graph integrity by preventing errors through real-time validation.

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Abstract

Techniques are disclosed for enabling cross-graph querying in graph databases using a transition operator within a traversal context. In some embodiments, a computing system receives a query containing a transition operator that facilitates transitioning from a first graph to a second graph. The system processes a traversal operation within the first graph, retrieves intermediary data, and transitions to the second graph to perform subsequent traversal operations. The query engine can utilize confidence scores associated with edges in the graphs to filter or prioritize results, ensuring the reliability of returned data. Additionally, data validation techniques using a mirror graph are disclosed, where new data is validated before integration into a production graph. These techniques streamline cross-graph data analysis, improve query efficiency, and maintain graph database integrity while supporting applications such as fraud detection, recommendation systems, and account linking.
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