Graph Neural Network Recurrence Classification Framework
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing predictive data analysis solutions face inefficiencies and reliability issues in performing predictive data analysis operations, particularly in handling graph-based data structures, which require complex computations for deriving insights.
Innovation Solution
The implementation of a graph-based recurrence classification machine learning framework that integrates simpler graph processing operations, such as subgraph generation and traversal, into training and inference operations using a graph neural network and recurrence classification model to reduce computational complexity and generate predictive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex graph processing operations are used for predictive data analysis, then measurement precision and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex graph processing task into distinct functional components: a graph neural network module that processes graph-based data structures to extract features, and a recurrence classification model that performs the actual predictive classification. This segmentation allows each module to specialize in specific operations, improving overall precision while managing complexity through modular architecture.
Solution Approach 2:
The graph neural network serves as an intermediary between the raw graph-based data structures and the recurrence classification model. It transforms complex graph data into processed features that the classification model can effectively utilize, thereby maintaining measurement precision while reducing the computational burden on the final classification stage.
2Measurement precision
If complex graph processing operations are performed, then predictive insight accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The graph neural network performs preliminary processing of graph-based data structures before the recurrence classification occurs. By pre-extracting relevant features and transforming the graph data into a more manageable format, the system maintains high predictive accuracy while significantly reducing the time required for the final classification operation.
3Reliability
If traditional predictive analysis methods are used, then implementation simplicity is maintained, but reliability and effectiveness decrease
Solution Approach 1:
The framework is designed with universal components that can handle various types of graph-based data structures through the graph neural network, while maintaining a consistent recurrence classification interface. This multi-functionality allows the system to achieve high reliability across different predictive analysis scenarios while managing complexity through a unified architectural approach.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using a graph-based recurrence classification machine learning framework that includes a graph neural network machine learning model and a recurrence classification machine learning model, where the recurrence classification machine learning model is configured to generate a predicted recurrence classification based at least in part on one or more graph-based features generated by a graph neural network machine learning model and one or more entity features associated with an entity identifier for an incoming event.