Social Graph Connectivity Scoring via Distributed Traversal
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Solution Overview
Problem
Determining quantifiable and meaningful connectivity within large network communities is challenging due to varying credibility and subjective nature of connectivity information, with individuals belonging to multiple communities and experiencing rapid changes in relationships, making it difficult to infer trustworthiness and make real-world decisions.
Innovation Solution
The system employs graph traversal and normalization techniques, including path counting and weighted link methods, to calculate connectivity ratings between nodes, using processing circuitry to assign and scale connectivity values based on subpaths and user-defined weights, and applies parallel computational frameworks for distributed calculations to handle changes in network dynamics.
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 information increases, but the difficulty of quantifying and comparing connectivity increases
Solution Approach 1:
The patent transforms qualitative connectivity information into quantitative metrics by introducing normalization techniques and mathematical models. Connectivity data from multiple sources is converted into standardized scores that can be compared across different community members, resolving the difficulty of quantifying diverse information.
Solution Approach 2:
The patent divides the complex task of determining overall connectivity into separate calculable components. By breaking down connectivity assessment into discrete measurable elements that can be individually processed and then aggregated, the system handles large quantities of information without overwhelming complexity.
2Adaptability or versatility
If connectivity information from multiple communities is considered, then the comprehensiveness of trust assessment improves, but the complexity of determination increases
Solution Approach 1:
The patent creates a universal connectivity framework that can process information from multiple different community types using the same mathematical models and normalization techniques. This multi-functional approach allows comprehensive assessment across diverse communities without requiring separate complex determination processes for each community type.
3Speed
If real-time connectivity changes are tracked to make prospective decisions, then the timeliness of decision-making improves, but the computational complexity increases
Solution Approach 1:
The patent pre-establishes mathematical models and normalization frameworks that can quickly process connectivity changes as they occur. By having the computational structure ready in advance, the system can rapidly assess real-time changes without undergoing complex setup procedures each time a decision is needed.
Data Source
AI summary
Systems and methods for social graph data analytics and node traversal are described herein. A social graph may comprise two or more nodes that each represents an individual, group, or entity, and links may connect the two or more nodes. A distributed graph storage/computation system may be configured to store node and link elements of one or more network communities in a distributed fashion. For example, the distributed graph storage/computation system may include a cluster registry, one or more node storage clusters, and one or more edge storage clusters. The cluster registry, node storage clusters, and edge storage clusters may each provide functions for providing node and link information and for traversing the social graph.