Social Graph Connectivity Ratings for Reliable Trust Assessment
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Solution Overview
Problem
Quantifying and comparing connectivity information within network communities is challenging due to varying credibility, subjective and objective data, and rapid changes in relationships, complicating trustworthiness and competence assessments.
Innovation Solution
Systems and methods utilize graph traversal and normalization techniques, including path counting and weighted links, to determine connectivity ratings, using parallel computational frameworks for efficient recalculations in response to network changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If connectivity information is collected from multiple community members to determine trustworthiness, then the assessment reliability improves, but the complexity of processing and comparing varying forms of connectivity information increases
Solution Approach 1:
The patent transforms diverse connectivity information into a standardized numerical representation called connectivity statistics. This involves converting various forms of connectivity data (direct connections, indirect paths, community memberships) into comparable numerical values that can be processed systematically. By changing the parameter representation from heterogeneous data formats to unified numerical statistics, the system maintains high assessment reliability while reducing processing complexity.
Solution Approach 2:
The patent introduces connectivity statistics as an intermediary layer between raw connectivity information and trustworthiness assessments. This intermediary transforms complex, multi-form connectivity data into standardized numerical representations that facilitate reliable comparison and assessment. The connectivity statistics serve as a mediator that simplifies the processing pipeline while preserving the essential trustworthiness evaluation capabilities.
2Reliability
If connectivity ratings are updated in real-time to reflect rapid changes in network relationships, then the assessment currency improves, but the computational resources required increase
Solution Approach 1:
The patent pre-calculates and stores connectivity statistics for all community members before assessments are needed. This preliminary computation includes determining direct connections, indirect paths up to specified depths, and community membership relationships. By performing these computations in advance, the system can rapidly update trustworthiness assessments when needed without requiring intensive real-time computational resources, thus maintaining assessment currency while reducing energy consumption.
Solution Approach 2:
The patent implements a dynamic update mechanism where connectivity statistics are recalculated only when specific changes occur in the network (such as new connections or community memberships). This selective recalculation approach allows the system to maintain current assessments without continuously processing all connectivity data, optimizing the balance between assessment currency and computational resource usage.
3Measurement precision
If the depth of path analysis is increased to capture indirect relationships, then the measurement precision improves, but the calculation time increases
Solution Approach 1:
The patent implements a configurable path depth parameter that allows the system to analyze indirect relationships up to a specified number of hops. By default, the system can be configured to analyze paths up to 2-3 levels of indirect connections, providing sufficient measurement precision for most trustworthiness assessments without the exponential computational cost of analyzing all possible path lengths. This partial action approach captures the most significant indirect relationships while limiting calculation time.
Solution Approach 2:
The patent pre-computes connectivity statistics including indirect path information up to the maximum required depth before assessments are performed. This preliminary action allows the system to have deep path analysis results readily available without performing lengthy calculations at the time of assessment, thus achieving high measurement precision while minimizing real-time calculation time.
Data Source
AI summary
Systems and methods for social graph data analytics to determine the connectivity between nodes within a community are provided. A user may assign user connectivity values to other members of the community, or connectivity values may be automatically harvested, calculated, or assigned from third parties or based on the frequency of interactions between members of the community. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph within the network community, or the degree of trust. The paths connecting a first node to a second node may be retrieved, and social graph data analytics may be used to determine a network connectivity value from all or a subset of all of the retrieved paths and/or one or more connectivity statistics value associated with the first node and/or the second node. A parallel computational framework may operate in connection with a key-value store to perform some or all of the computations related to the connectivity determinations. Network connectivity values and/or other social graph data may be outputted to third-party processes and services for use in initiating automatic transactions or making automated network-based or real-world decisions.