Graph Node Ranking by Property Criteria for Faster Network Analysis
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Solution Overview
Problem
Analyzing large graph networks, such as service mesh graphs with numerous nodes, is challenging due to the difficulty in determining the most heavily used parts, leading to suboptimal modifications and excessive computing resources.
Innovation Solution
A system ranks graph elements based on node property criteria, generating a ranking and determining a subset of nodes that satisfy a condition, allowing for highlighting or automatic changes to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a graph network includes many nodes and edges to represent complex relationships, then the representation completeness is improved, but the difficulty of determining important nodes and analysis time increases
Solution Approach 1:
The patent extracts and highlights only the important nodes that meet specified criteria from the large graph network, separating them from the rest of the nodes. This extraction approach maintains the completeness of the original graph representation while reducing analysis time by focusing attention only on the extracted important nodes rather than examining all nodes manually.
Solution Approach 2:
The patent applies visual highlighting (such as coloring or emphasizing) specifically to important nodes that meet the criteria, creating local quality differences in the visualization. This allows users to quickly identify important nodes through their distinct visual appearance without needing to analyze the entire graph, thus reducing analysis time while maintaining complete representation.
2Measurement precision
If manual analysis of each node is performed to determine importance, then the accuracy of node selection is improved, but the computing resources and time required increase
Solution Approach 1:
The system automatically evaluates nodes based on predefined criteria (such as degree centrality, betweenness centrality, or other importance metrics) and provides feedback by highlighting nodes that meet the thresholds. This automated feedback mechanism maintains accurate node selection based on objective metrics while dramatically improving analysis efficiency by eliminating manual evaluation of each node.
Solution Approach 2:
The system performs self-service by automatically identifying and highlighting important nodes based on the specified criteria without requiring manual analysis. The graph network system itself conducts the evaluation and selection process, using algorithms to compute node importance metrics and apply highlighting, thus maintaining precision while improving productivity.
3Reliability
If all nodes are analyzed to ensure no important node is missed, then the reliability of analysis results is improved, but the time and resources required increase excessively
Solution Approach 1:
Instead of analyzing all nodes equally, the patent applies partial action by focusing computational effort and visual attention only on nodes that meet the importance criteria. The system calculates metrics for all nodes but applies highlighting only to those exceeding thresholds, ensuring reliable identification of important nodes while reducing the effective analysis time by not requiring manual review of every node.
Data Source
AI summary
Graph elements can be ranked based on node property criteria. For example, a computing system can present, via a user interface of a user device, a graph that includes a plurality of nodes and a plurality of edges between the plurality of nodes. The plurality of nodes represent entities and the plurality of edges represent relationships between the entities. The computing device can receive a criteria related to node properties of the plurality of nodes for ranking the plurality of nodes in the graph. The computing device can generate a ranking of the plurality of nodes based on the criteria. The computing device can receive a condition associated with the ranking of the plurality of nodes and determine a subset of the plurality of nodes that satisfy the condition. The computing device can perform an action associated with the subset of the plurality of nodes that satisfy the condition.