Parallel Graph Traversal for Social Network Connectivity
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Solution Overview
Problem
Existing technologies face challenges in quantifying and meaningfully comparing connectivity and trustworthiness within large network communities, where members may have varying degrees of credibility and multiple community affiliations, leading to complex and dynamic connectivity changes.
Innovation Solution
The system employs graph traversal and normalization techniques, including path counting and weighted link approaches, to determine connectivity ratings between nodes within a network community. These methods involve counting subpaths, assigning relative user weights to links, and using parallel computational frameworks to recalculate weights and connectivity values in response to changes within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional connectivity measurement methods are used in large network communities, then the system can handle basic connectivity queries, but the system cannot effectively quantify and compare connectivity and trustworthiness across millions of members with varying credibility and multiple community affiliations
Solution Approach 1:
The patent transforms the complex qualitative assessment of connectivity and trustworthiness into quantitative parameters through graph traversal algorithms. By representing network members as nodes and relationships as edges with weighted values, the system converts subjective credibility assessments into measurable numerical parameters that can be precisely calculated and compared across the entire network community
Solution Approach 2:
The patent divides the large-scale network community into manageable components using graph theory segmentation. By breaking down the network into individual nodes (members) and edges (relationships), the system can process connectivity calculations in discrete units through parallel graph traversal operations, making the complex measurement problem tractable through systematic decomposition
2Reliability
If the system calculates connectivity ratings for all members in the network community, then the system provides comprehensive trustworthiness assessment, but the computational complexity increases significantly with network size
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing graph traversal results, connectivity ratings, and trustworthiness metrics in the database before they are needed for decision-making. When network relationships change, the system performs incremental updates rather than complete recalculations, significantly reducing the computational power required at query time while maintaining comprehensive and reliable assessments
Solution Approach 2:
The system employs feedback mechanisms where connectivity ratings and trustworthiness assessments are continuously updated based on changing network relationships. The database stores historical connectivity data that feeds back into the calculation algorithms, allowing the system to adapt to dynamic network changes while optimizing computational resource usage through pattern recognition and incremental updates
3Measurement precision
If the system uses detailed graph traversal and normalization techniques to accurately measure connectivity, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing graph traversal results, normalization factors, and connectivity metrics during off-peak periods or in the background. The system stores these pre-calculated values in the database, enabling rapid retrieval and comparison when connectivity assessments are needed, thus maintaining high measurement precision while minimizing processing time for end-user queries
Solution Approach 2:
The system implements dynamic processing where the level of graph traversal depth and normalization complexity adapts based on the specific query requirements and network state. For frequently accessed connectivity measurements, the system uses cached pre-computed values with simpler processing, while for novel or critical assessments, it performs more comprehensive graph traversals, dynamically balancing precision and processing time
4Measurement precision
If the system monitors and recalculates connectivity ratings in response to network changes, then the system maintains up-to-date trustworthiness information, but the system cannot provide timely prospective analysis
Solution Approach 1:
The patent implements preliminary action by continuously monitoring network changes and pre-calculating the impact of these changes on connectivity ratings before they fully propagate through the network. The system maintains a buffer of pre-computed connectivity adjustments that can be rapidly applied when changes occur, enabling both accurate real-time assessments and timely prospective analysis of potential future connectivity states
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 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 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. For example, a network connectivity value may be determined from all or a subset of all of the retrieved paths. Network connectivity values and/or other social graph data may be outputted to third-party processes, services, and ratings agencies for use in initiating automatic transactions, making automated network-based or real-world decisions, determining or verifying the identity of a node within the community, scoring or ranking nodes, or making credit-granting decisions.