Universal Information Flow Tracking Across Heterogeneous Networks
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Solution Overview
Problem
Current methods lack the capability to effectively analyze and track information flows in biological and social network systems, which are crucial for predicting behavior and managing complex systems, as they do not provide novel descriptor sets for objects or persons interacting within these dynamic networks.
Innovation Solution
A method is developed to create new descriptor sets by selecting primary and secondary network systems, identifying interacting nodes, subdividing networks, determining object interactions, and calculating edge density measurements to estimate information transfer capacity, using databases like Medline and ontologies from protein networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional information flow analysis methods are used, then computer network system analysis is effective, but biological and social network systems cannot be analyzed
Solution Approach 1:
The patent creates a universal information flow analysis framework that works across multiple network system types (computer networks, biological networks, social networks). The method uses standardized descriptor sets and interaction models that can be applied to any network system, enabling the same analytical approach to function universally across diverse domains while maintaining reliability through systematic characterization of information flows.
2Adaptability or versatility
If novel descriptor sets are created to track information flows in biological/social networks, then analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of information flow analysis into distinct components: defining network nodes, identifying interactions, creating descriptor sets for different network types, and analyzing information flows. This segmentation allows the complex problem to be broken down into manageable steps that can be systematically applied to biological, social, or computer networks without overwhelming complexity.
3Loss of information
If multiple interacting dynamic network systems are analyzed, then comprehensive understanding is achieved, but computational requirements increase
Solution Approach 1:
The patent extracts key interaction patterns and information flow characteristics from complex multi-network systems by identifying and analyzing specific descriptor sets. Rather than processing all possible interactions, the method extracts the essential information flows and interactions that define system behavior, reducing computational requirements while maintaining information completeness for predictive analysis.
Data Source
AI summary
A method of tracking information flows through multiple network systems includes selecting a primary network system from a population of primary and secondary network systems, wherein each of the primary and secondary network systems include network nodes, selecting first selected characteristic features that identify network nodes of the primary network system that provide interaction between the selected primary network system and secondary network systems, identifying at least one secondary network system that is capable of interacting with the network nodes of the primary network system, subdividing the primary network into subnetwork systems based on identifying primary network nodes that provide interaction between the primary network system and secondary network nodes, identifying the subnetwork systems that are capable of interacting with one or more network nodes of the secondary network systems, identifying a subnetwork node count of the primary network nodes in each subnetwork, identifying objects that are capable of interacting with the primary network nodes, and determining a coincidence frequency or a coincidence measurement between features of objects interacting with the primary network nodes and the features of the primary network nodes that indicate information exchanges between the primary and secondary network nodes.