Graph Analytics for User Reputation Assessment
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Solution Overview
Problem
Existing user evaluation methods rely on limited information, leading to inaccurate reputation assessments due to users' reluctance to provide negative information, resulting in artificially positive or inaccurate representations of their actual reputation.
Innovation Solution
A machine learning-based graph analytics system that stores user information in a graph datastore, processes feature vectors from user identifiers and historical activity, and generates reputation metrics using a trained machine learning model to determine access to resources or services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users are asked to provide information for evaluation, then the evaluation can be performed, but users may provide incomplete or artificially positive information, reducing measurement accuracy
Solution Approach 1:
The patent introduces graph analytics as an intermediary system that indirectly assesses user reputation by analyzing relationships and patterns in available data, rather than directly relying on user-provided information. The graph datastore and machine learning model act as mediators that infer reputation metrics from transaction patterns, device information, and behavioral data, thereby overcoming the problem of users providing incomplete or artificially positive information.
Solution Approach 2:
The patent replaces the mechanical approach of directly collecting user self-assessments with an automated machine learning-based graph analytics system. This substitution transforms the evaluation process from a manual, user-dependent mechanism to an automated system that processes graph data structures and generates reputation metrics through algorithmic analysis of relationships and patterns.
2Measurement precision
If more information is collected for evaluation, then reputation assessment accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the reputation evaluation system into distinct modular components: a graph datastore for storing structured relationship data, a machine learning model for processing, and specific evaluation metrics. This segmentation allows the complex system to be managed through separate, independently developable modules while maintaining overall system accuracy through their integrated operation.
Solution Approach 2:
The patent changes the fundamental parameters of the evaluation system by transitioning from traditional flat data structures to graph-based data structures with nodes and edges representing relationships. This parameter change enables the system to efficiently handle and analyze complex interconnected data while maintaining computational tractability through graph-specific algorithms and optimizations.
Data Source
AI summary
Aspects of the present disclosure relate to machine learning-based graph analytics for account evaluation. In examples, information associated with a user is stored in a graph datastore, which may include one or more account nodes and associated transaction nodes. Nodes within the graph datastore may be associated using edges that include identification information for an associated user. Accordingly, it may be possible to identify a subpart of the graph associated with a user that includes associated user identifiers and historical activity, which may be processed to generate a feature vector. The feature vector may be processed using a machine learning model to generate a set of reputation metrics for the user. The resulting set of reputation metrics may thus be used to determine whether to permit access to a resource or service by the user, or whether the user is permitted to create a new account, among other examples.