Network Topology Tuning via Cascade Probability Optimization
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Solution Overview
Problem
Existing network technologies fail to dynamically optimize network topology to enhance performance in changing conditions, such as in peer-to-peer networks where cascade probabilities affect the spread of behaviors among nodes, leading to suboptimal performance.
Innovation Solution
A method to calculate cascade probabilities for different network topologies, determine an optimal cascade probability, and adjust network topology or incentivize nodes to change their connections to align with this optimal probability, using regression analysis and control signals to optimize network performance iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network topology is adjusted to increase cascade probability, then the spread of successful behaviors improves, but the risk of copying unsuccessful or outdated behaviors increases
Solution Approach 1:
The system dynamically adjusts network topology parameters (connection probabilities, link weights) to optimize cascade probability. By changing these parameters based on real-time performance data and market conditions, the system achieves optimal balance between spreading successful behaviors and filtering unsuccessful ones.
Solution Approach 2:
The network topology is made dynamic rather than static. The system continuously monitors network performance and automatically adjusts connections to maintain optimal cascade probability under changing conditions, allowing the network to adapt to evolving market dynamics and behavior patterns.
2Speed
If more links are added to increase cascade probability, then behavior spread accelerates, but network complexity increases
Solution Approach 1:
Instead of simply adding more links, the system optimizes the parameters of existing links (connection probabilities, weights) to achieve desired cascade probability. This allows behavior spread acceleration without proportionally increasing network complexity or number of connections.
3Productivity
If network topology is dynamically adjusted, then performance optimization improves, but computational requirements increase
Solution Approach 1:
The network performs self-optimization by automatically monitoring its own performance and adjusting its topology without external intervention. This self-service approach eliminates the need for continuous manual tuning while maintaining optimal performance, reducing overall computational overhead.
Solution Approach 2:
The system implements feedback mechanisms where network performance data is continuously monitored and used to adjust topology. This closed-loop feedback allows the network to learn from its own operations and optimize performance automatically, reducing the need for extensive computational analysis and manual optimization.
Data Source
AI summary
In exemplary implementations of this invention, one or more computer processors receive electronic data indicative of, or compute (i) at least three different topologies of a network and (ii) a level of network performance of a task for each of the different topologies, respectively. The processors also calculate (i) a cascade probability for each of the different topologies, respectively, (ii) a curve indicative of correlation between the cascade probabilities and levels of network performance, and (iii) an optimal cascade probability which optimizes the level of network performance. A topological change in the network is produced (or its likelihood is increased). The topological change makes or would make the cascade probability closer to the optimal cascade probability. The processors output control signals (i) to make the topological change or (ii) to communicate an incentive for the topological change to an electronic node device in the network.