Graph Centrality Conversion Path Optimization
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Solution Overview
Problem
Existing computing applications face challenges in determining optimal paths in graph representations, especially as the number of nodes increases, leading to computational unfeasibility and inability to predict the effects of changes in node or edge graphs.
Innovation Solution
The method involves collecting user action data, generating separate graph representations for users with and without a specific property, executing centrality algorithms to identify central nodes, comparing these nodes to find unique central nodes, selecting actions represented by these nodes, and providing output via a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional path optimization methods are used in graph representations, then path finding can be performed, but computing resource utilization becomes excessively high and the process becomes computationally untenable as the number of nodes increases
Solution Approach 1:
The patent divides the graph into multiple communities or clusters based on node connectivity patterns. Instead of computing paths across the entire graph, the system segments the graph into manageable communities and performs path optimization only within relevant communities, dramatically reducing computational complexity and resource utilization while maintaining path finding accuracy.
Solution Approach 2:
The patent introduces a new dimension of analysis by computing node centrality measures (such as betweenness centrality) and using these to identify bridge nodes that connect different communities. This dimensional transformation allows the system to navigate between communities efficiently without exhaustive search, reducing computational burden while improving path optimization productivity.
2Loss of information
If traditional path optimization methods are used, then paths can be determined, but the system cannot predict the effects of changes to nodes or edges in the graph representation
Solution Approach 1:
The patent performs preliminary computation of node centrality measures and community assignments before path optimization is needed. These pre-computed metrics capture the structural importance of nodes and their relationships, enabling the system to predict how changes to nodes or edges will affect conversion paths without re-running complex optimizations, thus gaining predictive capability while managing system complexity.
Solution Approach 2:
The system uses centrality metrics and community structure as feedback mechanisms to understand graph dynamics. By monitoring changes in node centrality and community assignments, the system can predict the impact of modifications to the graph representation, providing actionable insights while maintaining manageable system complexity through focused metrics rather than comprehensive analysis.
3Quantity of substance
If the number of nodes in the graph representation increases, then more comprehensive data can be analyzed, but determining and optimizing paths becomes computationally untenable
Solution Approach 1:
The patent segments large-scale graphs into smaller communities based on node connectivity patterns. This segmentation allows the system to handle increasingly large numbers of nodes by processing only relevant local communities rather than the entire graph, maintaining path optimization productivity even as the total number of nodes grows.
Solution Approach 2:
The patent applies local quality analysis by computing centrality measures and identifying bridge nodes specifically at community boundaries where conversion paths are most likely to occur. This localized focus allows comprehensive analysis of large graphs while maintaining optimization speed by concentrating computational effort on structurally important regions rather than uniformly processing all nodes.
Data Source
AI summary
Methods of graph centrality-based conversion path optimization disclosed herein include determining central nodes of graph representations. Central nodes are determined separately for graph representations including a conversion node and separately for graph representations not including a conversion node. The nodes that are determined to be key nodes based on being central nodes for the graph representations including the conversion nodes but not for graph representations not including the conversion node may be used as candidate nodes. A candidate is selected from the candidate nodes, and an action associated with the candidate is presented to the user. An updated graph representation may be generated using data collected while the candidate is presented as a basis to determine updated key node candidates in a feedback loop.