Graph Node Extraction for Insight Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large graphs with numerous nodes and edges are difficult for humans to interpret due to the overwhelming amount of information, leading to analysts missing valuable insights.
Innovation Solution
A process that identifies insightful nodes by clustering graphs, determining anomalous and representative node attributes, and visually representing these nodes in a clustered graph format, using probability models and combinatorial calculations to expedite operations and highlight significant attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graphs are used to model relationships between entities, then the ability to understand system relationships is improved, but the difficulty of human interpretation increases due to the large number of nodes and edges
Solution Approach 1:
The patent extracts and highlights only the most insightful nodes from the graph by identifying anomalous values in node attributes. Instead of presenting the complete graph with all nodes and edges, the system extracts a subset of nodes that stand out based on their attribute distributions, making the information more manageable for human interpretation while preserving the most valuable insights.
Solution Approach 2:
The patent segments the graph analysis by dividing nodes into distinct categories: those with anomalous attribute values and those with representative values. This segmentation allows the system to present information in an organized manner, separating the most important nodes (anomalous ones) from the rest, thereby reducing the cognitive load on users while maintaining comprehensive analysis capability.
2Reliability
If comprehensive graph analysis is performed to extract all insights, then the completeness of analysis is improved, but the computational complexity and time required increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on calculating and comparing node attribute distributions rather than performing exhaustive graph analysis. The system computes statistical properties (mean, standard deviation) for each node's attributes and compares them locally to identify anomalies, which is much more efficient than comprehensive global analysis while maintaining high reliability in insight extraction.
Solution Approach 2:
The patent transforms the analysis approach by changing from examining individual node properties to examining statistical parameter distributions (mean, standard deviation) of node attributes. This parameter transformation enables efficient identification of anomalous nodes through simple statistical comparisons rather than complex computational graph analysis, significantly reducing processing time while maintaining analysis completeness.
3Measurement precision
If all node attributes are analyzed in detail, then the precision of insight identification is improved, but the complexity of data processing increases
Solution Approach 1:
The patent applies partial action by analyzing only the most relevant node attributes for identifying anomalies, rather than exhaustively processing all possible attributes. The system selectively computes statistical parameters for attributes that show variation and potential anomaly patterns, achieving high precision in insight identification while reducing processing complexity by focusing computational effort where it matters most.
Data Source
AI summary
Provided is a process, including: obtaining a clustered graph, wherein each of the nodes has a plurality of respective node attributes other than an identifier of the node; obtaining a designation of a given node attribute from among the plurality of node attributes; identifying a first subset of nodes of the graph as having anomalous values of the given node attribute by comparing values of the given node attribute in the first subset to a distribution of the given node attribute; identifying a second subset of nodes of the graph as having representative values of the given node attribute by comparing values of the given node attribute in the second subset to the distribution of the given node attribute; and sending instructions to a client device to display a representation of the graph.