Social Network Edge Selection for Content Spread
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Solution Overview
Problem
Current social networking platforms fail to maximize content spread within their networks, as existing connection recommendation algorithms focus on user similarity and mutual friends rather than optimizing content dissemination.
Innovation Solution
A method and system that determine a subset of edges in a social network to maximize content flow by generating samples, computing gain, and incrementing probability values for edges between similar users, ensuring each node has a specific number of incoming edges, thereby recommending connections that enhance content propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If connection recommendations are based on mutual friends and user similarity, then user connectivity increases, but content spread is not maximized
Solution Approach 1:
The patent changes the parameters used for connection recommendations from traditional metrics (mutual friends count, profile similarity) to influence-based metrics. Each user is assigned an influence score, and connections are recommended based on potential influence propagation, fundamentally changing the parameter space from static demographic data to dynamic influence measurements.
Solution Approach 2:
The patent introduces an influence propagation model as an intermediary layer between users and connection recommendations. This model acts as a mediator that calculates potential content spread through recommended connections, allowing the system to evaluate connection quality without directly computing complex content propagation scenarios for each possible connection.
2Quantity of substance
If content maximization is pursued directly, then content spread improves, but the problem becomes NP-hard and computationally intractable
Solution Approach 1:
The patent applies partial action by focusing on a subset of influential users and their connections rather than optimizing the entire network. By identifying and prioritizing users with high influence scores and their potential connections, the system achieves good content spread results without evaluating all possible network configurations, thus avoiding NP-hard complexity.
Solution Approach 2:
The patent segments the social network into influential users and regular users, and further segments connection recommendations into high-priority and low-priority categories. This segmentation allows the system to apply computationally intensive influence propagation calculations only to critical segments (influential users and their potential connections) while using simpler rules for other connections.
3Quantity of substance
If more connections are recommended to each user, then content availability increases, but user overload and connection quality decrease
Solution Approach 1:
The patent applies local quality by providing different numbers and types of connection recommendations to different users based on their influence scores and network positions. Influential users receive fewer but higher-quality recommendations focused on expanding their reach, while regular users receive more standard recommendations. This localized approach optimizes content availability while preventing user overload.
Data Source
AI summary
A method, a system and a computer program product for maximizing content spread in a social network are provided. Samples of edges are generated from an initial candidate set of edges. Each edge of the samples of edges has a probability value for content flow. Further, a subset of edges is determined from the samples of edges based on gain corresponding to each edge. Also, each node of the subset of edges is having at least one of less than ‘K’ or equal to ‘K’ incoming edges. Further, the probability of each edge, of the subset of edges, may be incremented. Furthermore, a final set of edges may be determined by ensuring ‘K’ incoming edges. The ‘K’ incoming edges may be ensured by removing one or more incoming edges when a number of the incoming edges for a node of the final set is greater than ‘K’ incoming edge.