Expand description
§Graph Statistics and Analysis for Dependency Graphs
Comprehensive statistical analysis and metrics computation for code dependency graphs. This module provides detailed insights into graph structure, connectivity patterns, and characteristics that are essential for understanding codebase architecture.
§Key Features
- Degree Distribution Analysis: In-degree, out-degree, and total degree statistics
- Connectivity Metrics: Strongly connected components, graph density, clustering
- Structural Patterns: Hub detection, authority identification, bottleneck analysis
- Import Relationship Insights: Dependency depth, fan-in/fan-out analysis
- Performance Characteristics: Memory usage, computation complexity estimates
- Comparative Analysis: Before/after graph evolution tracking
Structs§
- Analysis
Metadata - Analysis execution metadata
- Circular
Dependency - Circular dependency information
- Connectivity
Analysis - Graph connectivity and component analysis
- Degree
Distribution - Detailed degree distribution statistics
- Degree
Stats - Statistical measures for degree sequences
- Dependency
Path - Important dependency path
- Graph
Analysis Results - Comprehensive graph analysis results
- Graph
Statistics Analyzer - Main graph statistics analyzer
- Import
Insights - Import relationship specific insights
- Node
Info - Information about important nodes
- Performance
Profile - Performance characteristics of the graph
- Statistics
Config - Configuration for statistics computation
- Storage
Efficiency - Storage and memory efficiency metrics
- Structural
Patterns - Structural patterns and notable nodes
- Traversal
Complexity - Complexity analysis for graph algorithms