Social Network Influence Ranking via Map-Reduce Segmentation
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Solution Overview
Problem
Identifying influential users within social networking communities is challenging due to the large population and diverse interactions, as existing methods lack efficient techniques to quantify and rank user influence effectively.
Innovation Solution
Employing power iteration techniques in conjunction with a map-reduce framework to analyze user relationships and calculate an influence score, which partitions datasets into shards for parallel processing and consolidates results to determine influential users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify influential users in large social networking communities, then the analysis can be performed with simple algorithms, but the processing time and computational resources increase significantly due to the large population and diverse interactions
Solution Approach 1:
The patent divides the large social networking community into smaller sub-communities or clusters based on user interaction patterns and relationships. This segmentation allows the influence analysis to be performed on smaller, more manageable subsets of data, reducing the overall processing time while maintaining accuracy through aggregated results from multiple segments.
Solution Approach 2:
The patent pre-calculates and stores intermediate influence metrics such as user connection counts, interaction frequencies, and community membership data before performing the final influence ranking. This preliminary processing reduces the computational burden during actual influence analysis, enabling faster query response times for identifying influential users.
2Measurement precision
If comprehensive user relationship data is analyzed to accurately quantify influence, then the influence ranking becomes more precise, but the device complexity and computational requirements increase
Solution Approach 1:
The patent extracts and focuses on the most critical relationship metrics that correlate with user influence, such as the number of connections, interaction frequency, and community centrality, while ignoring less relevant data. This extraction approach maintains ranking accuracy by capturing essential influence indicators without processing the entire complexity of all user interactions.
Solution Approach 2:
The patent transforms complex user relationship data into simplified influence scores by applying weighted calculations that convert multiple relationship parameters (connection count, interaction frequency, community size) into a single composite influence metric. This parameter transformation reduces system complexity while preserving the nuanced information needed for accurate influence ranking.
3Reliability
If the entire user dataset is processed to identify influential users, then complete community coverage is achieved, but the processing efficiency and scalability decrease
Solution Approach 1:
The patent divides the complete user dataset into multiple segments or batches that can be processed independently and in parallel. Each segment is analyzed for influential users, and the results are aggregated to achieve complete community coverage. This segmentation enables scalable processing that maintains reliability while improving efficiency through distributed computation.
Solution Approach 2:
The patent initially identifies influential users by processing a representative subset of the community data that captures the essential influence patterns, then applies these findings to infer influence rankings for the entire community. This partial processing approach achieves sufficient coverage for practical purposes without the computational cost of analyzing every single user interaction.
Data Source
AI summary
A system includes a memory storing a dataset representing a community of users of a social networking service, the social networking service providing a platform enabling a user within the community to communicate with at least one of a plurality of other users within the community. The system includes a processor coupled to the memory, the processor configured to determine a ranking of the service users in the dataset based on an initial influence score for at least one of the users. The processor is also configured to determine a contribution to a first user within the community by at least one second user of the plurality of other users within the community, and calculate an influence score for the first user. The ranking is revised based at least in part on the calculated influence score, and information is rendered to a target user based on the revised ranking.