Social Graph Connectivity Analytics for Dynamic 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 forms, and rapid changes in individual or entity relationships, complicating real-world decision-making.
Innovation Solution
Systems and methods for determining connectivity using graph traversal and normalization techniques, including path counting, weighted links, and parallel computational frameworks to calculate trust ratings and update connectivity values dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If connectivity information is collected from multiple community members to determine trustworthiness, then the quantity and diversity of data increases, but the difficulty of quantifying and comparing connectivity information increases
Solution Approach 1:
The patent transforms qualitative connectivity information into quantitative trust scores by changing the parameter representation. It uses statistical parameters (mean, standard deviation) to normalize connectivity data from different sources and scales, enabling meaningful comparison across diverse community members while maintaining the quantity of information collected.
Solution Approach 2:
The patent introduces an intermediary computational framework that processes raw connectivity information from multiple members. This intermediary system performs graph traversals, normalization, and statistical calculations to bridge the gap between diverse qualitative data and comparable quantitative trust metrics.
2Reliability
If connectivity information is updated in real-time to reflect rapid changes in relationships, then the reliability of trust assessments improves, but the computational complexity and time required for analysis increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing graph traversals and normalization factors before actual trust assessments are needed. It establishes baseline connectivity metrics and statistical parameters in advance, so that when relationships change, only incremental updates are required rather than complete re-analyses.
Solution Approach 2:
The patent implements dynamic trust scoring that adapts to changing connectivity relationships. It uses statistical measures (standard deviation, mean) that automatically adjust as new connectivity information arrives, allowing the system to maintain reliability while managing computational complexity through efficient updating mechanisms.
3Measurement precision
If statistical measures such as standard deviation are used to assess connectivity reliability, then the precision of trust scoring improves, but the loss of time required for computation increases
Solution Approach 1:
The patent applies partial action by computing statistical measures selectively rather than for all possible node pairs. It calculates mean and standard deviation for relevant subsets of connectivity data that directly impact the trust assessment, avoiding unnecessary computations while maintaining precision where it matters most.
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.