Graph Exploration Module for Multi-Modal Data Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing visual representation techniques for large graph data sets become overly complex and confusing, failing to effectively convey patterns and trends due to their complexity.
Innovation Solution
A graph exploration module that generates and presents various chart types, including hybrid-type bar charts with aggregated nodes and edges, allowing users to switch between chart types and interactively explore subgraphs through pivot, filtering, and cloning operations, while providing a visual link between exploration panels to track user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional point-and-line visual representation is used for large graph data sets, then the complete graph structure can be displayed, but the visualization becomes overly complex and confusing
Solution Approach 1:
The patent segments the graph data into multiple subgraphs based on connectivity thresholds. Highly connected nodes are separated into a first subgraph, while less connected nodes form a second subgraph. This segmentation reduces the complexity of each individual visualization while preserving the complete graph structure across multiple views.
Solution Approach 2:
The patent introduces a new dimensional parameter - connectivity threshold - to differentiate between subgraphs. By varying this threshold parameter, the system creates multiple visualization layers that can be explored interactively, transforming a single complex 2D visualization into a multi-dimensional exploration space.
2Quantity of substance
If traditional point-and-line visual representation is used for large graph data sets, then all nodes and edges can be shown, but patterns and trends become difficult to identify
Solution Approach 1:
By segmenting the graph into subgraphs with different connectivity characteristics, the patent makes pattern detection feasible. Each subgraph can be analyzed independently for its specific patterns, rather than searching for patterns in the overwhelming complexity of the complete graph.
Solution Approach 2:
The patent applies partial action by initially focusing visualization on the most highly connected subgraph, allowing users to detect patterns in the core structure first. Users can then progressively explore other subgraphs as needed, rather than attempting to analyze all graph elements simultaneously.
3Adaptability or versatility
If multiple chart types are provided for graph visualization, then users can explore data in different ways, but the system complexity increases
Solution Approach 1:
The patent implements a universal charting module that can generate multiple types of charts (bar charts, pie charts, line charts, etc.) from the same subgraph data. This multi-functional approach provides visualization flexibility without requiring separate specialized systems for each chart type, thereby controlling system complexity.
Data Source
AI summary
A graph exploration module is described which displays graph data (or any data set) using any one of an extensible collection of chart types. Some of the chart types may present aggregated results associated with the graph data. One chart type provides bars which represent aggregations of nodes in the graph data and a collection of links which represent relationships among the bars. The graph exploration module may present the chart in the context of an interactive exploration panel within an exploration canvas. A user can make various selections which prompt the graph exploration module to generate a new exploration panel, together with a link which connects to the new panel to the previous panel. This process can be repeated any number of times to produce one or more exploration paths which reveal a history of exploration actions made by the user(s). That history can be saved and later retrieved.