Network Node Relevance Quantification via Local Neighborhood Segmentation
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Solution Overview
Problem
Current methods for measuring the relevance of individual network nodes in large networks are limited, as they only identify the most influential nodes and do not quantify node relevance accurately, and their accuracy depends on the network topology and sampling methods.
Innovation Solution
A computer-implemented method that determines a node's relevance by measuring its contribution to overall network flows based on local neighborhood structure, using the expected force (ExF) metric, which estimates the node's influence through the number of edges and transmission clusters, and can be computed efficiently and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centrality indicators (degree, eigenvalue centrality, Page Rank) are used to identify influential nodes, then the most highly influential nodes can be identified, but the measures are only informative for the top 1/10th of 1 percent of nodes and do not quantify node relevance accurately
Solution Approach 1:
The patent segments the network analysis into local neighborhood computations rather than global computations. By dividing the network into local neighborhoods around each node and computing relevance metrics independently for each neighborhood, the method achieves accurate node relevance quantification without requiring complex global network analysis, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
Each node computes its own relevance metric by analyzing its local neighborhood structure independently. This self-service approach allows nodes to determine their relevance without requiring centralized complex computations, achieving both accurate quantification and computational efficiency that reduces overall system complexity
2Measurement precision
If global network analysis is performed to determine node relevance in large networks (>1 billion nodes), then accurate relevance measures can be obtained, but the computation becomes increasingly difficult and time-consuming
Solution Approach 1:
The patent divides the large network into local neighborhoods around each node, allowing independent computation of relevance metrics for each node based only on its local structure. This segmentation enables parallel computation across millions or billions of nodes simultaneously, reducing total computation time from infeasible to practical while maintaining accuracy
Solution Approach 2:
The patent pre-defines the relevance metric formulation based on local neighborhood structure properties that can be computed independently. By establishing the computational framework in advance that relies only on local connectivity information, the system can process large networks efficiently without requiring iterative global convergence, thus minimizing computation time
3Ease of operation
If centrality measures are used to rank nodes, then node ordering can be achieved, but the accuracy depends on network topology, sampling methods, and the type of diffusive process
Solution Approach 1:
The patent applies local quality by designing the relevance metric to depend only on the local neighborhood structure around each node rather than global network properties. This local approach makes the measurement robust to variations in overall network topology and sampling methods, as each node's relevance is determined by its immediate connectivity patterns which are more stable and less sensitive to global changes
Solution Approach 2:
The patent changes the fundamental parameter from global centrality metrics to local neighborhood-based metrics. By reformulating the relevance measurement to use only local structural parameters (such as the number and connectivity of neighboring nodes within a certain radius), the system achieves both ease of ranking and improved reliability that is independent of global topology variations
Data Source
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AI summary
A computer-implemented method for determining a relevance of a node in a network, comprises the steps of obtaining a digital representation of a local neighborhood structure of the node in the network in a computer-readable non-volatile memory; determining a numerical value characteristic of the node's relevance, and outputting the numerical value to a user. According to the invention, the numerical value is determined based on the neighborhood structure of the node.