Graph Edge Transposition for Balanced Search Node Connectivity
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Solution Overview
Problem
Conventional graph update techniques using a single session of edge inversion lead to inconsistent numbers of output edges, resulting in inappropriate graph generation and potential search failures.
Innovation Solution
A two-session edge transposition process is employed to invert edge directions in a target graph, first generating a first graph with a uniform number of input edges and then a second graph with a suppressed number of output edges, ensuring a balanced edge distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If one session of edge inversion is performed to control the number of input edges to each node, then the number of input edges can be controlled, but the number of output edges from each node may vary, leading to failure in generating an appropriate graph
Solution Approach 1:
The patent divides the single edge inversion process into two separate sessions: first inverting edges to control input edge counts, then inverting edges again to control output edge counts. This segmentation allows independent optimization of both input and output edge distribution, resolving the contradiction between controlling input edges and maintaining reliable search performance.
Solution Approach 2:
The patent performs preliminary edge inversion to create an intermediate graph state before finalizing the graph structure. By pre-processing the edge directions in two stages, the system ensures both input and output edge counts are appropriately controlled before the graph is used for search operations, preventing search failures.
2Productivity
If the number of output edges from each node is allowed to vary, then the graph can be generated quickly, but this leads to high processing loads and search failures
Solution Approach 1:
The patent segments the edge inversion into two controlled sessions, where each session uniformly distributes edges. This prevents the creation of nodes with excessive output edges that would cause high processing loads, while still allowing efficient graph generation through automated processing.
Solution Approach 2:
The patent changes the parameter control approach by applying edge inversion twice with different objectives: first controlling input edge count, then controlling output edge count. This parameter transformation ensures balanced edge distribution that reduces processing load while maintaining generation efficiency.
3Device complexity
If conventional single-session edge inversion is used, then the process is simple and fast, but it results in inconsistent edge distribution and inappropriate graph generation
Solution Approach 1:
The patent divides the complex graph generation process into two simpler, sequential edge inversion sessions. Each session has a specific, manageable objective (controlling input edges, then output edges), making the overall complex process more controllable and precise while maintaining reasonable simplicity.
Data Source
AI summary
A generation apparatus acquires a target graph, being a graph in which a plurality of nodes corresponding to a plurality of objects to be a target of data search is linked by edges, extracts output edges of a first number from among output edges having each node as a start point, targeted for each of the plurality of nodes included in the target graph, and then generates a first graph in which the plurality of nodes is linked by an edge obtained by inverting the direction of the extracted output edge, and output edges of a second number from among output edges having each node as a start point, targeted for each of the plurality of nodes included in the first graph, and then generates a second graph in which the plurality of nodes is linked by an edge obtained by inverting the direction of the extracted output edge.