Network Topology Analysis via Centrality Scoring
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Solution Overview
Problem
Existing methods for analyzing large and dynamic social network topologies are computationally expensive and often require traversal of the entire network, using private information, and result in uninformed or overly broad advertising recommendations.
Innovation Solution
A method and device for analyzing network topologies that determine centrality parameters and generate topology scores for each node, identifying relatively significant nodes without traversing the entire network, using abstracted unique identifiers and focusing on relationships rather than private information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network traversal methods are used to analyze network topologies, then comprehensive network analysis is achieved, but computational expense and time consumption increase significantly
Solution Approach 1:
The patent segments the network analysis task by dividing nodes into different levels based on their connectivity patterns. Level 0 nodes are those with no connections, Level 1 nodes have connections only to Level 0 nodes, and so on. This segmentation allows the system to analyze only relevant portions of the network for each query, avoiding complete network traversal while maintaining analysis accuracy for the specific query context.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing connectivity information, node levels, and relationship data in a structured format before queries are made. This preliminary organization of network data enables rapid retrieval and analysis during actual queries, eliminating the need for repeated full-network traversals and significantly reducing analysis time.
2Measurement precision
If traditional network traversal methods are used to analyze network topologies, then comprehensive network analysis is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the network analysis task by dividing nodes into different levels based on their connectivity patterns. Level 0 nodes are those with no connections, Level 1 nodes have connections only to Level 0 nodes, and so on. This segmentation allows the system to analyze only relevant portions of the network for each query, avoiding complete network traversal while maintaining analysis accuracy for the specific query context.
Solution Approach 2:
The patent applies partial action by performing network analysis only on the necessary subset of nodes relevant to each specific query, rather than traversing the entire network. The system determines the appropriate analysis scope based on query type and node relationships, executing only the minimal required computations to answer each query accurately, thereby reducing overall computational expense.
3Loss of information
If private or semi-private node information is used for network analysis, then detailed node characteristics are obtained, but user privacy is compromised
Solution Approach 1:
The patent extracts and uses only the minimal necessary relationship information (connectivity patterns, node levels, and topological relationships) required for network analysis, while deliberately excluding private or semi-private node attributes such as personal identifiers, demographic data, or sensitive user information. This extraction approach maintains analysis capability while protecting user privacy.
Solution Approach 2:
The patent changes the parameters used for analysis from private node attributes to topological relationship parameters such as node levels, connectivity counts, and relationship patterns. By transforming the analysis basis from content-based private information to structure-based public information, the system achieves effective network analysis without compromising user privacy.
4Productivity
If traditional network analysis methods are used, then advertising recommendations are generated, but the recommendations are uninformed or overly broad
Solution Approach 1:
The patent applies local quality by generating advertising recommendations that are tailored to the specific local context of each node's position and relationships within the network. Instead of applying uniform broad-casting strategies, the system analyzes the local topological characteristics (node level, connectivity patterns, community structure) to create targeted recommendations appropriate for each node's specific position and influence scope in the network.
Solution Approach 2:
The patent replaces traditional mechanical network traversal methods with a computational approach based on pre-calculated topological parameters and scoring mechanisms. The system uses centrality scores, proximity metrics, and relationship-based algorithms to generate informed advertising recommendations, substituting exhaustive search methods with efficient computational models that provide both accuracy and scalability.
Data Source
AI summary
A method, non-transitory computer readable medium, and apparatus for discovering and analyzing a network topology includes obtaining information regarding a network including a relationship of a plurality of nodes of the network. At least one value for each of a plurality of centrality parameters is determined for each of the plurality of nodes. At least one topology score for each of the plurality of nodes is generated based on one or more of the centrality parameter values. One or more relatively significant nodes are identified based on one or more of the topology scores and output.