Graph Relationship Visualization Using Similarity-Weighted Node Scoring
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Solution Overview
Problem
Current graph visualization methods struggle to accurately represent relationships between nodes, especially in large datasets, leading to clutter and computational inefficiencies, and different similarity metrics have varying assumptions and parameters, making them unsuitable for diverse data types.
Innovation Solution
A method and system that determine relationship counts, generate similarity coefficients, and calculate weighted scores to represent the strength of relationships between nodes, using machine learning algorithms to improve accuracy and efficiency in identifying and presenting complex relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional graph visualization methods are used to represent relationships between nodes in large datasets, then the visualization can be generated, but the accuracy of relationship representation deteriorates and computational efficiency decreases
Solution Approach 1:
The patent segments the relationship analysis by identifying common relationships between pairs of nodes and calculating similarity coefficients for each relationship type separately. This segmentation allows the system to process complex graphs by breaking down relationship analysis into manageable components (different relationship types), improving both accuracy and efficiency.
Solution Approach 2:
The patent transforms relationship strength representation by introducing similarity coefficients and weighted scores as new parameters. Instead of using raw relationship counts, the system applies parameter transformations (similarity coefficients based on common relationships divided by total relationships) to create a more accurate and computationally efficient representation of node relationships.
2Measurement precision
If detailed relationship counts are calculated for all node pairs, then relationship accuracy improves, but memory requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for relationship representation by calculating similarity coefficients based on common relationships. Instead of storing and processing all possible relationship details, the system extracts the key metric (similarity coefficient) that captures relationship strength, reducing memory requirements while maintaining accuracy.
3Adaptability or versatility
If multiple similarity metrics with different parameters are used to analyze diverse data types, then analysis comprehensiveness improves, but system complexity increases
Solution Approach 1:
The patent creates a universal relationship analysis framework that works across diverse data types by defining a common similarity coefficient calculation method. The system handles different relationship types (edges) through a unified approach of counting common relationships and calculating similarity coefficients, making the system adaptable to various graph structures without requiring separate complex metrics for each data type.
Data Source
AI summary
A method for identifying relationships between nodes in a graph is provided. The method comprises determining a count of relationships for each relationship type associated with each node in a pair of nodes on the graph. The relationship types are represented by edges connected to the nodes in the pair of nodes. A number of common relationships are determined between the pair of nodes for each relationship type. A count for each common relationship is determined for the number of common relationships between the pair of nodes. A similarity coefficient for each common relationship is determined based on the count for each common relationship and the count of relationships for each relationship type associated with each node in the pair of nodes. A weighted score is generated to represent strength for relationships between the pair of nodes using the similarity coefficients.