Social Graph Connectivity Analytics for Community Trust
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Solution Overview
Problem
Determining quantifiable and meaningful connectivity within network communities is challenging due to varying degrees of credibility and subjective information, making it difficult to compare trustworthiness and competence across members, especially when connections change rapidly and across multiple communities.
Innovation Solution
Systems and methods using graph traversal and normalization techniques, such as path counting and weighted links, to determine connectivity ratings, with processing circuitry configured to count paths, assign user weights, and recompute values in response to changes, employing parallel computational frameworks like Apache Hadoop or Google MapReduce for distributed computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If connectivity information is collected from multiple community members, then the quantity and diversity of data increases, but the difficulty of comparing and quantifying trustworthiness increases
Solution Approach 1:
The patent transforms qualitative connectivity information into quantitative metrics by introducing normalized connectivity scores and trustworthiness parameters. This allows diverse information from multiple members to be compared and aggregated mathematically, resolving the difficulty of comparing subjective trust assessments across different scales and formats.
Solution Approach 2:
The patent applies normalization techniques that bring different connectivity measurements onto a common scale or reference framework. By establishing equipotential comparison standards, the system enables meaningful aggregation and comparison of trustworthiness metrics across diverse data sources and community members.
2Adaptability or versatility
If connectivity analysis is performed across multiple communities, then the scope of analysis increases, but the complexity of determining quantifiable representation increases
Solution Approach 1:
The patent divides the complex multi-community analysis into manageable segments by processing each community separately and then aggregating results. This segmentation allows the system to handle multiple communities systematically, breaking down the overall complexity into smaller, tractable computational tasks that can be performed and combined.
Solution Approach 2:
The patent implements a universal connectivity analysis framework that can operate across different community types and structures. The system uses standardized algorithms and data models that adapt to various community configurations, providing multi-functional capability without requiring separate complex systems for each community type.
3Measurement precision
If connectivity ratings are updated in real-time, then the accuracy of prospective analysis improves, but the computational resources required increase
Solution Approach 1:
The patent implements periodic or event-driven updates of connectivity ratings rather than continuous real-time computation. The system recalculates metrics at scheduled intervals or in response to specific triggering events, maintaining adequate accuracy for prospective analysis while significantly reducing computational resource consumption compared to continuous updating.
Solution Approach 2:
The patent applies partial updates that recalculate only the portions of connectivity ratings affected by recent changes, rather than performing complete recalculations. This selective approach maintains measurement precision for affected nodes while avoiding the excessive computational resources required for full system-wide recomputation.
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 assigned from third parties or based on the frequency of interactions between members. Connectivity values may represent such factors as alignment, reputation, status, and/or influence within a social graph of a 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 performed on the retrieved paths. 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.