Seed Group Selection in Probabilistic Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Designing a viral marketing campaign to maximize content dissemination across a network is challenging due to the complexity of selecting a seed group of users with varying levels of influence, making it difficult to predict and adjust probabilities for effective content propagation.
Innovation Solution
A method for selecting a seed group of users based on an influence matrix, where users with high influence probabilities are iteratively added and their probabilities adjusted to reorder influence levels, allowing for improved content dissemination across communication networks like the internet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If selection algorithms are used to identify influential users for viral marketing, then content dissemination is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the complex seed selection problem into iterative steps: (1) initialize user influence scores, (2) iteratively select users with highest scores, (3) update influence scores of remaining users based on selections, and (4) repeat until seed group is complete. This segmentation transforms an intractable combinatorial optimization problem into a manageable iterative process with polynomial time complexity.
Solution Approach 2:
The patent performs preliminary computation of user influence scores based on network structure and content characteristics before the actual seed selection process. By pre-calculating these scores and updating them iteratively, the system avoids re-computing from scratch during each selection step, significantly reducing overall computational complexity while maintaining selection quality.
2Productivity
If a large number of users are considered for seed selection to maximize content spread, then content dissemination is improved, but the difficulty of detecting and measuring influence increases
Solution Approach 1:
The patent introduces an intermediary influence scoring mechanism that translates complex social network relationships into a single measurable metric for each user. This score incorporates network position, connection strength, and content relevance, providing a simplified yet comprehensive measure of user influence that can be efficiently computed and compared across large user populations.
Solution Approach 2:
The patent replaces manual or complex qualitative influence assessment with an automated computational model that calculates influence scores based on observable network data. This substitution allows for systematic measurement of user influence across large scales, transforming subjective judgment into objective, reproducible metrics that can be processed algorithmically.
3Productivity
If iterative probability adjustment is performed to optimize seed group selection, then content dissemination is improved, but loss of time increases due to multiple adjustment cycles
Solution Approach 1:
The patent performs a limited number of iterative adjustments rather than exhaustively optimizing the seed group selection. By conducting a fixed number of iterations (e.g., 3-5 passes) through the selection and update process, the system achieves sufficient optimization without the diminishing returns of excessive iterations, balancing solution quality with computational efficiency and time consumption.
Data Source
AI summary
Determined seed groups herein improve content dissemination across a communication network connecting a plurality of users. Probabilities of each user in the plurality influencing remaining users in the plurality to observe the content are identified to select a first influential user from the plurality. The seed group size is established and a user of the plurality with a probability proximate to the probability of the first influential user is identified. Based on the seed group size, the probabilities of the remaining users in the plurality are unified with the probability of the first influential user to determine new probabilities of the remaining users, and another user of the plurality with a probability proximate to the probability of the first influential user is identified. The method then provides for selecting the users identified as having probabilities proximate to the probability of the first influential user to establish the seed group.