Graph Visualization Grouping and Link Navigator
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Solution Overview
Problem
Existing systems struggle to provide a scalable and interactive way for users to visualize and navigate large graphs with thousands to millions of nodes and links, limiting user understanding and requiring excessive effort to manage detail and context.
Innovation Solution
A scalable architecture that allows users to interact with large graphs by grouping nodes and links, providing a rolled-up view, enabling incremental navigation, and using a link navigator tool to explore details while maintaining an overview, with features like recursive grouping, cross-group detail, and navigation history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the graph is displayed with all nodes and links visible, then complete information is available, but the visualization becomes overwhelming and difficult to navigate
Solution Approach 1:
The patent applies segmentation by dividing the large graph into hierarchical groups and clusters. Nodes are organized into parent-child group relationships, allowing the graph to be broken down into manageable sections. This enables users to view the graph at different levels of abstraction without losing the underlying complete information, as groups can be expanded or collapsed as needed.
Solution Approach 2:
The patent introduces an additional dimensional layer by implementing hierarchical grouping structure on top of the existing graph. This creates a multi-level visualization where users can navigate through groups and clusters, adding a hierarchical dimension to the flat graph structure. This extra dimension allows information to be organized and presented more effectively without losing completeness.
2Ease of operation
If the graph is simplified by hiding details, then navigation becomes easier, but important information may be lost
Solution Approach 1:
The patent implements dynamic visualization where groups and clusters can be interactively expanded and collapsed by users. This dynamic behavior allows the graph to adapt to user needs - simplifying the view when navigation is needed and showing details when information is required. The hierarchical structure maintains all information while allowing flexible presentation states.
Solution Approach 2:
The system provides self-service by automatically organizing nodes into hierarchical groups based on their relationships, without requiring manual intervention. The grouping structure is generated dynamically from the graph data itself, allowing the system to serve its own organization needs while preserving all underlying information for when users need to explore details.
3Device complexity
If hierarchical grouping is applied to reduce complexity, then the graph becomes more manageable, but the structure and relationships may become less clear
Solution Approach 1:
The patent uses hierarchical groups as intermediary elements between individual nodes. These groups act as mediators that preserve and represent the relationships between nodes while reducing overall complexity. The group structure maintains the connectivity information, allowing users to understand relationships at different levels of abstraction without losing the underlying connection data.
Solution Approach 2:
The patent implements nested hierarchical grouping where groups can contain sub-groups, creating a nested structure similar to Russian dolls. This nesting allows complex relationships to be organized in nested layers, with each level representing a different granularity of the same underlying data. The nested structure preserves all relationships while organizing them in a more manageable hierarchical format.
Data Source
AI summary
Scalable architecture for managing and rendering a large graph containing a large number of nodes and edges. The user can group arbitrary nodes to encapsulate complexity without losing any of the cross-group edges dependencies. All edges of the nodes contained in the group are rolled up into roll-up links (or “arteries”) where the graphical thickness indicates relatively how many links are bundled. By collapsing groups the entire view gives the user a clearer understanding of the graph. Information related to the groups and links is retained for drill-into to obtain the details inside each group on the same canvas and for user navigation across groups.