Social Graph Visualization with Trust Score Rendering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Visualizing large network communities is challenging due to edge crossings, resource-intensive calculations, and difficulty in depicting node importance, especially on hardware like smartphones, where rendering becomes slow.
Innovation Solution
Systems and methods for visualizing relationship data in social graphs using graph traversal and normalization techniques, such as path counting and weighted links, to determine connectivity ratings and trust scores, allowing for meaningful visualization of network communities by selecting relevant contacts and calculating their spatial layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all connections between members of a network community are visualized, then complete relationship data is displayed, but edge crossings increase causing lines to be 'blacked out' and visualization becomes meaningless
Solution Approach 1:
The patent extracts and removes edges from the visualization based on calculated importance metrics. By computing connectivity ratings and trust scores for each edge, the system selectively displays only those edges that contribute most meaningfully to the overall graph structure, eliminating redundant or low-value connections that would create visual clutter and edge crossings.
Solution Approach 2:
The patent applies different visualization qualities to different parts of the graph based on local connectivity characteristics. Edges are rendered with varying prominence, thickness, or visibility depending on their calculated importance, allowing the visualization to highlight critical relationships while de-emphasizing less significant ones, thereby maintaining clarity even in dense network regions.
2Ease of manufacture
If uniform depiction of nodes is used, then simplicity of rendering is maintained, but individual or entity importance within the network community is not conveyed
Solution Approach 1:
The patent varies node visualization properties such as size, color, or border thickness based on calculated importance metrics like trust scores and connectivity ratings. This allows the rendering to convey meaningful information about each node's significance within the network while maintaining a consistent visual language across all nodes.
3Measurement precision
If connectivity calculations between each node are performed, then accurate trust scores are obtained, but resource intensive computations slow down rendering on hardware like smartphones
Solution Approach 1:
The patent performs connectivity calculations selectively rather than comprehensively for all node pairs. By focusing computations on subsets of nodes and edges that are most relevant to the current visualization context or user interests, the system achieves sufficient trust score accuracy while dramatically reducing the computational burden on mobile devices.
Solution Approach 2:
The patent pre-calculates and stores connectivity metrics, trust scores, and edge importance values before the actual visualization rendering occurs. This preliminary computation phase allows the rendering process to simply display pre-processed data, significantly improving real-time performance on resource-constrained hardware while maintaining accurate measurements.
Data Source
AI summary
Systems and methods for visualizing trust scores in a social graph are described herein. The trust scores may be determined within or between individuals, groups, and/or entities connected in a network. A user may initiate the visualization of connections between themselves and others in the network. Contacts are selected for the visualization of the social graph, and a spatial distribution of the nodes is calculated. Nodes in the visualization of the social graph are rendered based on the calculated spatial distribution. Connections between the nodes may be rendered based on connectivity statistics calculated between the rendered nodes.