Social Network Influence Graph Construction
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Solution Overview
Problem
Existing techniques for determining influence in social networks are limited to individual user-based measurements, which fail to ensure that information from influential users reaches all users in the network, limiting the propagation of information across social networks.
Innovation Solution
A system that calculates influence scores between users by creating influence vectors from user data, including activity and profile information, and constructs an influence graph to facilitate interaction among users, using these scores as edge weights to identify paths and target influential users for information propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual user-based influence measurements are used, then the measurement process is simple, but the ability to ensure information propagation across the network is limited
Solution Approach 1:
The patent transitions from one-dimensional individual user influence scores to two-dimensional influence graphs that map relationships between multiple users. By representing users as nodes and influence relationships as weighted edges, the system captures network topology and propagation paths, enabling accurate assessment of how information spreads across the entire network rather than just measuring individual influence in isolation.
Solution Approach 2:
The patent introduces influence graphs as an intermediary structure between individual user data and propagation effectiveness measurement. The graph serves as a mediator that integrates user attributes, connection patterns, and influence scores to model information flow, allowing the system to evaluate propagation across the network without requiring direct observation of every information transmission event.
2Productivity
If influence graphs with path-finding techniques are used to target influential users, then information propagation effectiveness is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary construction of influence graphs and pre-computation of influence metrics before information propagation campaigns. By building the graph structure ahead of time and pre-identifying influential users and optimal paths, the system avoids real-time computational complexity during actual information dissemination, enabling rapid deployment while maintaining propagation effectiveness.
Solution Approach 2:
The patent replaces brute-force trial-and-error information propagation with algorithmic path-finding techniques on influence graphs. Instead of randomly or uniformly distributing information, the system uses graph algorithms to identify optimal propagation paths through influential users, substituting computational algorithms for mechanical trial-and-error approaches and achieving higher efficiency with manageable complexity.
Data Source
AI summary
The disclosed embodiments provide a system that facilitates user interaction. During operation, the system obtains user data for a first user and a second user connected to the first user in a social network. Next, the system uses the user data to calculate one or more influence scores between the first and second users. The system then constructs an influence graph of a set of users comprising the first and second users by creating a first node representing the first user, creating a second node representing the second user, and using the one or more influence scores as edge weights of directed edges between the first and second nodes. Finally, the system uses the influence graph to facilitate interaction among the users.