Graph Data Visualization Layer Segregation
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Solution Overview
Problem
Large graph visualization techniques struggle to effectively represent and analyze datasets with hundreds or thousands of vertices due to occlusion and data density issues, making it difficult for users to gain a summarized understanding of the data.
Innovation Solution
The approach involves displaying graph data as layers, where vertices are automatically segregated based on filter criteria, allowing users to create and manage layers dynamically, such as by hop count, to visually organize and filter vertices, thereby alleviating the complexity of large graph visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If graph visualization techniques are used to represent large datasets, then relationships between data entities can be visualized, but occlusion and data density become too high for effective visualization
Solution Approach 1:
The patent segments the large graph data into multiple interactive layers, where each layer represents a filtered subset of vertices and edges. Users can selectively display and interact with specific layers based on their analysis needs, transforming an overwhelming single-layer visualization into manageable segmented views that reduce occlusion while preserving access to the complete dataset.
2Ease of operation
If pan-and-zoom is used to explore large graphs, then users can see details of specific areas, but users see just a part of the graph instead of getting an overview
Solution Approach 1:
The patent introduces a new dimensional organization by stacking multiple graph layers vertically or spatially, allowing users to navigate both within individual layers (maintaining detail view) and across layers (providing overview). This multi-layered spatial arrangement enables simultaneous access to both granular details and holistic context without the trade-off inherent in traditional pan-and-zoom approaches.
3Ease of operation
If clustering/communities is used to group vertices, then vertices can be visually distinguished and visualized closer together, but the method focuses on visualizing each vertex and edge rather than providing filtered views
Solution Approach 1:
The patent extracts specific subsets of vertices and edges from the complete graph and places them into separate filterable layers based on user-defined criteria. This extraction approach allows users to isolate and analyze specific patterns, relationships, or data subsets without being overwhelmed by the complete graph structure, effectively reducing visualization complexity while maintaining analytical depth.
4Ease of operation
If fish-eye view is used to magnify selected areas, then the selected area is magnified and surrounding areas are distorted to remain in view, but the method still visualizes all vertices and edges with distortion
Solution Approach 1:
The patent implements dynamic layer visibility and scaling, where users can activate or deactivate specific layers and adjust their individual display parameters independently. This dynamic control allows users to focus on specific areas or relationships by displaying only relevant layers at appropriate scales, eliminating the need for fish-eye distortion while maintaining both focused detail and contextual awareness through selective layer management.
Data Source
AI summary
Embodiments visualize graph data including vertices interconnected by edges. Embodiments receive a selection of a source vertex and generate a filtered layer corresponding to a filter condition relative to the source vertex. Embodiments automatically place a plurality of vertices that correspond to the filter condition on the filtered layer.