network-analysis

# Network Analysis Skill

Map and analyze social network structures using graph theory methods and specialized visualization tools.

## Overview

The Network Analysis skill enables mapping and analysis of social network structures using centrality measures, community detection algorithms, and visualization tools like Gephi, UCINET, or igraph for understanding relational patterns in social systems.

## Capabilities

### Network Mapping
- Data collection methods
- Edge list construction
- Adjacency matrix creation
- Network boundary definition
- Multi-mode networks

### Centrality Analysis
- Degree centrality
- Betweenness centrality
- Closeness centrality
- Eigenvector centrality
- PageRank and variants

### Community Detection
- Modularity optimization
- Hierarchical clustering
- Block modeling
- Clique detection
- Core-periphery structure

### Network Metrics
- Density and connectivity
- Clustering coefficient
- Path length measures
- Reciprocity and transitivity
- Structural holes

### Visualization
- Gephi workflows
- UCINET procedures
- igraph in R/Python
- Layout algorithms
- Dynamic visualization

## Usage Guidelines

### When to Use
- Mapping relationships
- Identifying key actors
- Detecting communities
- Analyzing diffusion
- Understanding structure

### Best Practices
- Define boundaries clearly
- Document data collection
- Select appropriate metrics
- Validate interpretations
- Visualize effectively

### Integration Points
- Quantitative Methods skill
- Qualitative Analysis skill
- Survey Design and Administration skill
- Mixed Methods Integration skill

## References

- Social Network Analysis process
- Statistical Analysis Pipeline process
- Computational Social Scientist agent