Affinity Network Management via Real-Time Graph Scoring
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Solution Overview
Problem
Existing network management systems fail to dynamically measure and optimize the performance of participants based on their roles, responsibilities, and requirements within affinity networks, leading to inefficient transaction processing and resource utilization.
Innovation Solution
A method and system that models and manages affinity networks by processing real-time transaction data to update affinity scores between nodes, selecting target nodes for transactions based on these scores, and presenting responses to users for optimal transaction suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time transaction data is processed to dynamically update affinity scores, then network performance optimization is improved, but system complexity increases
Solution Approach 1:
The system pre-establishes a graph data structure with nodes representing participants and edges representing relationships. Affinity scores are pre-calculated and stored for each edge, allowing rapid selection without complex real-time computations during transaction processing.
Solution Approach 2:
The patent introduces an affinity score as an intermediary metric that mediates between raw transaction data and participant selection. This score aggregates multiple factors (transaction history, communication patterns, performance metrics) into a single value, simplifying the selection process while maintaining optimization capability.
2Measurement precision
If affinity scores are updated in real-time based on transaction history, then participant selection accuracy is improved, but data processing time increases
Solution Approach 1:
The system maintains pre-computed affinity scores for all participant pairs in the network. When a transaction request arrives, the system immediately queries the existing affinity score rather than calculating it from scratch, enabling instant selection based on historical performance data.
Solution Approach 2:
The system continuously updates affinity scores based on actual transaction outcomes and participant performance feedback. Successful transactions increase affinity scores between participants, while failed transactions decrease them, creating a self-learning system that improves accuracy over time without requiring complex real-time analysis.
Data Source
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Figure 3A~3C
AI summary
A method models and manages affinity networks. The method includes receiving real-time transaction data; processing a transaction of the real-time transaction data to identify a source node and a target node of a graph; and processing the transaction to update an affinity score of an edge from the source node to the target node. The method further includes receiving a request; selecting, responsive to the request, the target node using the affinity score after updating the affinity score; and presenting a response using the target node.