Social Influence Valuation via Network Segmentation
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Solution Overview
Problem
Existing technologies face challenges in measuring and valuing the influence of individuals within social networks, as well as their impact on others, which is crucial for understanding their commercial value and social impact.
Innovation Solution
A computer system that calculates the influence of each user by analyzing their interactions, activities, and relationships across a network, using data mining models to estimate both intrinsic and influence values, and presenting these values in a table or graphical form for better decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to assess influence, then the assessment process is simple, but the measurement precision is insufficient
Solution Approach 1:
The patent segments the influence assessment into multiple independent components: network structure analysis, interaction pattern detection, activity correlation measurement, and value computation. Each component processes specific aspects of influence separately, then integrates results to achieve comprehensive measurement precision while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces computational models and algorithms as intermediary layers between raw social network data and influence assessments. These intermediaries (data mining models, statistical algorithms) transform complex multi-dimensional data into quantifiable influence metrics, enabling precise measurement without requiring direct complex analysis of all social interactions
2Measurement precision
If comprehensive data analysis is performed to value influence, then the value accuracy improves, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing social network data to extract relevant features, pre-computing network structures, and preparing interaction patterns before actual influence valuation. This preliminary preparation organizes raw data into structured formats, enabling faster and more accurate influence computations when needed without re-processing entire datasets
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most influential users and key interaction patterns rather than uniformly analyzing all users and all interactions. By identifying and prioritizing significant influence relationships, the system achieves accurate valuation of top influencers with reduced computational time compared to exhaustive analysis of entire networks
3Loss of information
If influence values are computed for all users, then the completeness of information improves, but the data processing complexity increases
Solution Approach 1:
The patent applies local quality by computing influence values with different levels of detail for different users based on their roles and impact. Highly influential users receive comprehensive analysis with detailed metrics, while less influential users receive simplified assessments. This differentiated approach maintains information completeness for critical users while reducing processing complexity for the overall system
Data Source
AI summary
The social influence that each person in a computer network system exercises over others in the system may be valued by aggregating the differences in value of each of the others to the network both with and without the person being present. This calculated influence may be used as a basis for charging advertisers for advertisements to the users, as well as for providing preferential treatment to users that exert the greatest influence.