| name | network-analysis |
| description | Map and analyze social network structures using centrality measures, community detection, and visualization tools like Gephi or UCINET |
| allowed-tools | ["Read","Write","Edit","Grep","Glob","Bash"] |
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
