Clustered Graph Visualization for Semantic Similarity Analysis
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Solution Overview
Problem
Existing graph analysis techniques struggle to effectively extract insightful nodes from large graphs due to the overwhelming amount of information, often requiring users to choose between fine-grained investigation of individual nodes or insights from larger collections, and lack efficient computational methods to identify anomalous or representative nodes within commercially relevant time frames.
Innovation Solution
The development of a process that generates graphical visualizations of clustered graphs, allowing for both fine-grained context and larger structural insights, using computational techniques to accelerate visualization generation, and identifying insightful nodes by determining visual attributes and positions based on node and edge properties, with automated identification of attributes and nodes exhibiting anomalous or representative behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If graph analysis techniques are applied to large graphs with thousands of nodes and edges, then insights can be extracted from the data, but the computational complexity and time required increase significantly
Solution Approach 1:
The patent segments the graph into clusters of nodes based on semantic similarity, allowing the system to process and visualize grouped data rather than individual nodes. This segmentation reduces the effective complexity by organizing information into manageable units that can be analyzed collectively, enabling faster insight extraction from large graphs.
Solution Approach 2:
The patent extracts and highlights specific insightful nodes and clusters from the larger graph structure. By identifying and separating the most important elements (such as nodes with anomalous or representative behavior), the system can present condensed information that maintains analytical value while reducing the visual and computational burden of processing the entire graph.
2Measurement precision
If detailed visualization of individual nodes is provided, then fine-grained investigation is enabled, but the ability to see larger structural insights is compromised
Solution Approach 1:
The patent implements hierarchical visualization that segments the graph into multiple levels: individual nodes, clusters of nodes, and the overall graph structure. Users can navigate between these levels to access fine-grained details when needed while maintaining an overview of structural patterns, thus resolving the contradiction between detailed and broad perspectives.
Solution Approach 2:
The patent employs a nested visualization approach where cluster icons represent groups of nodes, and individual node icons can be accessed within clusters. This nesting allows the system to display aggregated cluster information at one level of detail while enabling drill-down to individual node details at deeper levels, maintaining both structural insights and fine-grained analysis capability.
3Loss of time
If automated identification of insightful nodes is implemented, then analysis time is reduced, but the complexity of the analysis algorithm increases
Solution Approach 1:
The patent employs algorithms that analyze specific parameters such as node degree, betweenness centrality, and semantic similarity metrics to automatically identify insightful nodes. By transforming the complex task of manual analysis into automated parameter-based scoring and clustering, the system reduces analysis time while managing algorithmic complexity through well-defined computational criteria.
Data Source
AI summary
Provided is a process including: obtaining a clustered graph, the clustered graph having three or more clusters, each cluster having a plurality of nodes of the graph, the nodes being connected in pairs by one or more respective edges; determining visual attributes of cluster icons based on amounts of nodes in clusters corresponding to the respective cluster icons; determining positions of the cluster icons in a graphical visualization of the clustered graph; obtaining, for each cluster, a respective subset of nodes in the respective cluster; determining visual attributes of node icons based on attributes of corresponding nodes in the subsets of nodes, each node icon representing one of the nodes in the respective subset of nodes; determining positions of the node icons in the graphical visualization based on the positions of the corresponding cluster icons of clusters having the nodes corresponding to the respective node icons; and causing the graphical visualization to be displayed.