Network Contagion Management via Spreading Models
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Solution Overview
Problem
Existing computer networking systems face inefficiencies and inaccuracies in detecting, anticipating, and managing the spread of accurate communications, particularly struggling with misinformation due to complex and impractical approaches that are limited to small networks, leading to computational infeasibility as network sizes increase.
Innovation Solution
The implementation of a contagion management system that dynamically identifies information dispersion using spreading models, modifies network connectivity settings to curb misinformation spread, and incorporates global effects to improve accuracy and efficiency in managing network communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems use complex approaches to detect and manage misinformation spread, then measurement precision improves, but device complexity increases and becomes computationally infeasible for large networks
Solution Approach 1:
The patent segments the network evaluation process into modular components: identifying nodes with target characteristics, generating global effect factors, determining estimated node counts using spreading models, and comparing against thresholds. This segmentation transforms the intractable exhaustive evaluation into manageable discrete steps that can be executed efficiently even in large networks.
Solution Approach 2:
The patent changes the evaluation parameters by introducing global effect factors and using spreading models with configurable parameters (starting set size, spreading factors, network connectivity). This allows the system to evaluate communication spread accuracy by adjusting these parameters rather than performing exhaustive computations, making the system feasible for large-scale networks.
2Measurement precision
If existing systems perform exhaustive checks on network nodes to assess misinformation spread, then measurement precision improves, but productivity decreases due to computational infeasibility
Solution Approach 1:
The patent performs preliminary actions by identifying an initial set of nodes with target characteristics and generating global effect factors before conducting the full evaluation. The spreading model uses these preliminary results to estimate the number of infected nodes, avoiding the need for exhaustive checks on all nodes and significantly improving computational speed while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by evaluating only the necessary portion of the network through spreading models rather than performing exhaustive checks on all nodes. The system determines the estimated number of nodes with target characteristics using mathematical models that require fraction of the computational resources of a complete exhaustive search, thus improving productivity while maintaining measurement precision.
3Reliability
If existing systems enforce strict conditions such as network continuity and convexity to manage contagion, then reliability improves, but adaptability decreases due to inability to handle heterogeneity
Solution Approach 1:
The patent applies local quality by allowing different nodes in the network to have different characteristics and by computing global effect factors that account for local heterogeneity. The spreading model can handle varying network connectivity, different spreading factors for different node types, and heterogeneous local effects without requiring the network to satisfy strict continuity or convexity conditions, thus improving adaptability while maintaining reliability through the threshold comparison mechanism.
4Device complexity
If existing systems do not account for global effects in network spread evaluation, then device complexity decreases, but measurement precision worsens due to inaccurate evaluation
Solution Approach 1:
The patent introduces a universal global effect factor that can be applied across different network configurations and contagion scenarios. This single mechanism handles both local and global effects uniformly, allowing the system to account for worldwide trends and external influences without requiring separate complex evaluation systems. The global effect factor integrates seamlessly with the spreading model, improving measurement precision while adding minimal complexity.
Data Source
AI summary
The present disclosure relates to systems, methods, and computer-readable media for improving the detection, evaluation, and management of computer networks with regard to providing and managing the dispersion of communications. For example, in one or more implementations, a contagion management system can dynamically, efficiently, and quickly identify the extent to which information is expected to disperse in a network based on local and global factors, including virality. In various implementations, the contagion management system can utilize one or more spreading models to determine the potential expected network spread of network information and characteristics as well as can modify network connectivity settings to mitigate network spreading. In this manner, the contagion management system can effectively manage contagions of misinformation, or other characteristics, that spread throughout computer networks.