Network Graph
Network Graph is a Cortex-side job responsible for constructing and analyzing network structures through the generation of scoped subgraphs and the application of quantitative scoring mechanisms. The job accepts input parameters that define analytical boundaries and scope constraints, then produces isolated subgraph representations that focus on relevant network regions. This scoped approach enables focused analysis of specific network areas without requiring full-graph processing, which improves computational efficiency when working with large-scale network data.
Subgraph Generation and Scope
The job generates scoped subgraphs by applying parameter-defined constraints to establish which nodes and edges should be included in analysis. These subgraphs represent bounded portions of a larger network structure, allowing the system to isolate relevant components based on specified criteria. By limiting analysis to these scoped regions rather than processing entire networks, the job reduces computational overhead while maintaining analytical precision for targeted investigations.
Scoring Mechanisms
Network Graph applies quantitative scoring to both nodes and edges within generated subgraphs. These scoring systems provide numerical assessments that reflect the characteristics, relationships, and importance of network components. The scoring output enables downstream processes to prioritize, filter, or rank elements based on their computed values, supporting decision-making and further analytical workflows that depend on comparative measurements of network structure.