Directed Network Cycle Detection for Accurate Node Classification
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Solution Overview
Problem
Existing methods for analyzing network nodes in directed networks are often inaccurate in determining the relationship and classification of nodes.
Innovation Solution
A method for detecting a directed network that involves acquiring flow direction feature information, calculating layer scores based on the presence or absence of directed cycles, and using these scores to determine node types in the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network node analysis methods are used, then the analysis process is simple, but the accuracy of determining node relationships and classification is low
Solution Approach 1:
The patent segments the network analysis process into distinct components: constructing a directed graph from network data, detecting cycles in the graph, calculating layer scores for each node, and classifying nodes based on these scores. This segmentation allows each component to be optimized independently, improving overall accuracy while managing complexity systematically
Solution Approach 2:
The patent performs preliminary actions by first constructing a directed graph representation of the network and detecting cycles before proceeding to node classification. This preliminary structuring of the data enables more accurate subsequent analysis by establishing the topological relationships and identifying cyclic patterns that affect node classification
2Measurement precision
If cycle detection and layer score calculation are performed for all nodes, then node classification accuracy is improved, but the computational time and complexity increase
Solution Approach 1:
The patent applies partial action by focusing the computationally intensive cycle detection and layer score calculation operations only on nodes that are part of or affected by cycles in the directed graph. Rather than uniformly processing all nodes, the method identifies cyclic components and concentrates analysis efforts there, reducing overall computational time while maintaining accuracy for critical nodes
Data Source
AI summary
A method for detecting node types in a directed network is provided. In the method, flow direction feature information of data traffic between network nodes in the directed network is acquired. The flow direction feature information includes a directed graph of nodes representing the network nodes. Layer scores of the nodes in the directed graph are determined based on whether the directed graph includes a directed cycle. The node types of the network nodes represented by the nodes in the directed network are determined according to the layer scores of the nodes.