Network Entity Ranking Verification via Social Graph Analysis
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Solution Overview
Problem
Current systems lack efficient methods for exchanging and tracking goods and services among network entities, and there is a need to verify the validity of entity rankings to prevent artificial inflation or deflation, which is not effectively addressed by existing technologies.
Innovation Solution
A network entity and exchange system that assigns ranking metrics based on transactions and feedback, uses social graph analysis to verify ranking validity, and allows entities to create virtual companies for economies of scale, leveraging social networks for monetization and mentoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ranking metrics are assigned based on transactions and feedback, then entity rankings become more accurate and reliable, but the system complexity increases due to multiple metrics and verification mechanisms
Solution Approach 1:
The ranking system is segmented into multiple independent components: transaction-derived metrics, user-derived metrics, social graph analysis, and verification mechanisms. Each component operates independently and contributes to the overall ranking, allowing the system to maintain high reliability while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediary elements such as the social graph analysis layer and verification mechanisms that mediate between raw transaction data and final rankings. These intermediaries process and validate information, ensuring ranking reliability while abstracting the complexity from the core ranking function.
2Measurement precision
If social graph analysis is used to verify ranking validity, then artificial inflation or deflation of rankings is prevented, but the time and computational resources required increase
Solution Approach 1:
The social graph analysis is performed preliminarily and continuously in the background, establishing verification rules and relationships before ranking verification is needed. This allows the system to quickly check pre-computed social graph metrics rather than performing exhaustive analysis each time a ranking needs verification.
Solution Approach 2:
The system changes parameters by using aggregated social graph metrics and pre-computed relationships rather than performing real-time individual analysis. This parameter transformation from detailed individual verification to aggregated metric verification reduces computational time while maintaining verification accuracy.
3Reliability
If multiple ranking metrics are collected from transactions and feedback, then the ranking system becomes more comprehensive and accurate, but the difficulty of detecting and measuring genuine rankings increases
Solution Approach 1:
The system implements feedback loops where rankings are continuously monitored and verified against multiple metrics including transaction data, user feedback, and social graph analysis. This multi-layered feedback mechanism detects anomalies and genuine rankings more effectively by cross-validating across different data sources.
Solution Approach 2:
The patent creates a composite ranking system that combines multiple different metric types (transaction-derived, user-derived, social graph-based) into a unified ranking framework. This composite approach detects genuine rankings more effectively by looking for consistency across diverse metric sources rather than relying on a single measurement method.
Data Source
AI summary
A system receives a selection, by a first entity, of product, provided by a second entity, wherein the first entity and the second entity are members of an entity network. The system receives an indication of receipt of the product via an application programming interface (API), transmitted by a first entity, and derives a first ranking metric for the second entity of the entity network based on feedback received from the first entity of the entity network. The system derives a second ranking metric for the second entity of the entity network based on one or more transactions received from a third entity of the entity network, determines whether the first ranking metric is valid based on the second ranking metric, and provides the first ranking metric to entities of the entity network based on the validity of the first ranking metric.