Spiral Graph Visualization Reducing Clutter
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Solution Overview
Problem
Existing visualization techniques for large graph data are ineffective due to visual clutter and limited scalability, and they primarily focus on community structures rather than visualizing individual node centrality measures.
Innovation Solution
A processor-implemented method generates a visualization of large graph data using spirals to reduce visual clutter. This involves community detection, configuring nodes by centrality, determining spirals with the highest centrality node at the center, and creating a coarse graph using super nodes and edges to arrange spirals on a force-directed layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing visualization techniques (chord diagrams, node-link diagrams) are used to display large graph data, then community structures can be represented, but visual clutter increases and scalability is limited for graphs with hundreds or thousands of nodes
Solution Approach 1:
The patent segments the graph visualization into multiple hierarchical levels: (1) communities are detected and grouped together, (2) each community is represented by a representative node, and (3) the overall graph is divided into multiple pages or views. This segmentation reduces visual clutter by organizing nodes into manageable groups while preserving community structure information.
Solution Approach 2:
The patent introduces spatial dimensionality by arranging communities in a two-dimensional layout that reflects their hierarchical relationships. Communities are positioned in space based on their connectivity and importance, allowing the visualization to scale to large graphs by utilizing spatial arrangement rather than simply increasing node density in a flat view.
2Loss of information
If existing visualization techniques focus on community structures, then interconnections between communities can be shown, but individual node centrality measures (closeness, eigenvector centrality) cannot be effectively visualized
Solution Approach 1:
The patent applies local quality by computing and visualizing different centrality measures for different nodes within their respective communities. Each node's visual properties (such as size, color, or position within the community) are adjusted according to its specific centrality metrics, allowing individual node importance to be highlighted while maintaining the overall community structure.
Solution Approach 2:
The patent merges multiple visualization objectives into a unified approach: community detection algorithms identify communities, centrality measures are computed for nodes within communities, and both pieces of information are integrated into a single visualization where community structure and node centrality are simultaneously represented through coordinated graphical elements.
3Quantity of substance
If more nodes are added to the graph to represent larger networks, then network coverage improves, but visual clutter increases and existing techniques become ineffective
Solution Approach 1:
The patent segments large graphs into smaller community-based units that can be individually understood and analyzed. By detecting communities and representing them as cohesive groups, the visualization maintains interpretability even as network coverage expands to include thousands of nodes, since users can focus on one community at a time rather than attempting to comprehend the entire graph at once.
Solution Approach 2:
The patent uses dimensional organization to scale the visualization to large networks by arranging communities in a spatial hierarchy. This allows the system to accommodate increased network coverage while maintaining ease of operation, as the spatial arrangement provides intuitive navigation and context for understanding node relationships across the entire network.
Data Source
AI summary
A system and method for visualizing large graph data using spirals are provided. The method includes (i) generating communities from an input graph data, (ii) configuring nodes in the communities in an ascending order based on a centrality measure, (iii) determining a spiral for the communities by configuring (a) a node with a highest centrality measure at a spiral center and (b) the nodes in a spiral shape based on the ascending order of the centrality measure along the spiral shape, (iv) determining a first super edge for the spiral of the communities, (v) generating a coarse graph using super nodes, the first super edge, and a second super edge, (vi) configuring, using a standard force-directed layout algorithm, corresponding spirals of the communities on the generated coarse graph based on a weight of the second super edge and attractive forces to generate the visualization of the large graph data.