Network Graph Reputation Assessment via Whitelist Blacklist Connections
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Solution Overview
Problem
As network interactions between entities increase, gauging an entity's reputation, such as trustworthiness or creditworthiness, becomes challenging, especially when entities are unfamiliar, as self-reporting is unreliable and third-party verification often provides limited and dubious metrics.
Innovation Solution
Systems and methods that utilize network graphs to estimate metrics about entities by analyzing their connections to pre-classified 'whitelist' and 'blacklist' nodes, combining scores to generate reputation or creditworthiness scores, and applying these scores to authorize actions or transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If self-reporting is used to assess entity reputation, then the assessment process is simple and quick, but the reliability of the reputation metric deteriorates because bad entities can misrepresent their reputation
Solution Approach 1:
The patent introduces network connection data as an intermediary indicator to assess entity reputation. Instead of directly relying on self-reported reputation data, the system uses third-party network connection information (whitelist/blacklist nodes) as a mediator to indirectly evaluate an entity's trustworthiness, thereby resolving the contradiction between assessment speed and reliability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network interactions and updating reputation assessments based on observed connections. The reputation metric is dynamically adjusted based on feedback from network data, allowing the system to maintain reliable assessments while processing information efficiently
2Reliability
If third-party verification services are used to assess entity reputation, then the reliability of reputation assessment improves, but the complexity of the assessment system increases and the metrics remain limited
Solution Approach 1:
The patent creates a universal reputation assessment system that can evaluate multiple types of entities (users, businesses, devices) across different networks using the same methodology. The system performs multiple functions including reputation assessment, risk evaluation, and transaction authorization, reducing the need for separate verification systems for different purposes
Solution Approach 2:
The system enables entities to automatically receive reputation assessments based on their network connections without requiring manual verification requests. The assessment process operates autonomously by continuously analyzing network data, reducing system complexity while maintaining high reliability through automated third-party verification
3Measurement precision
If network connection data is analyzed to assess entity reputation, then the reliability and depth of reputation metrics improve, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent segments the complex network data into manageable components: whitelist nodes (trusted entities), blacklist nodes (untrusted entities), and connection strength metrics. This segmentation allows the system to process large volumes of network data efficiently while maintaining high measurement precision in reputation assessments
Solution Approach 2:
The system transforms complex network connection data into simplified reputation parameters by changing the data representation. Network connections are converted into quantifiable metrics (connection strength, trust scores) that can be processed efficiently, maintaining measurement precision while reducing processing complexity
Data Source
AI summary
Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.