User Influence Ranking via Connectedness and Interactivity Scores
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Solution Overview
Problem
Social networking platforms face challenges in effectively identifying and ranking influential users based on their connectivity and activity levels, which hinders targeted advertising and user engagement strategies.
Innovation Solution
The system employs a social networking monitor that calculates connectedness and interactivity scores for users by analyzing their direct and indirect connections, interactions, and restricted relationships, using algorithms to generate a composite user rank that reflects their influence and popularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If social networking platforms analyze user connectivity and activity levels to identify influential users, then advertising effectiveness and user engagement improve, but system complexity and computational requirements increase
Solution Approach 1:
The system segments user influence measurement into distinct components: connectedness score (based on network structure and connections) and interactivity score (based on user activity and engagement). These segmented metrics can be calculated independently and combined to produce a composite influence ranking, reducing overall system complexity while maintaining advertising effectiveness
Solution Approach 2:
The system performs preliminary calculations of connectedness and interactivity scores for all users, storing these pre-computed values for later use in influence ranking. This preliminary action allows the system to avoid complex real-time calculations during advertising deployment, reducing operational system complexity while maintaining effectiveness
2Measurement precision
If the system calculates connectedness and interactivity scores for all users, then user ranking accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary computation of connectedness and interactivity scores for all users and stores these pre-calculated values. When influence ranking is needed, the system retrieves these pre-computed scores rather than calculating them in real-time, significantly reducing processing time while maintaining ranking accuracy
Solution Approach 2:
The system calculates comprehensive connectedness and interactivity scores for all users (excessive action) to ensure accurate influence ranking, but then uses only the necessary portions of these scores for specific advertising decisions, optimizing the balance between measurement precision and processing efficiency
3Adaptability or versatility
If the platform monitors and analyzes user interactions and connections, then user engagement strategies improve, but data processing complexity and infrastructure requirements increase
Solution Approach 1:
The system segments data processing into distinct modules: one for monitoring user interactions, another for calculating connectedness scores based on network structure, and a third for computing interactivity scores based on engagement patterns. This segmentation allows each component to be optimized independently, reducing overall data processing complexity while enhancing engagement strategy adaptability
Solution Approach 2:
The system introduces intermediate summary statistics (connectedness score and interactivity score) that aggregate complex user interaction data into manageable metrics. These intermediate values serve as mediators between raw interaction data and final engagement decisions, reducing data processing complexity while maintaining strategic adaptability
Data Source
AI summary
Methods, apparatus, and articles of manufacture to rank users in an online social network are described. An example method to rank in an online social network includes determining a connectedness of a user on a social networking site based on a number of contacts of the user, determining a number of first interactions directed from the user to at least one of the contacts, determining a number of second interactions associated with the first interaction and at least one of the contacts, and ranking the user with other users on the social networking site based on the connectedness, the first interactions, and the second interactions.