social-network-analysis

What problem does it solve?

Social Network Analysis helps you turn relational data (who is connected to whom) into measurable structures you can explain, compare, and visualize.

Core Features & Use Cases

• Network construction from data: Build graphs from edge lists, adjacency matrices, or text co-occurrence networks to represent social ties.
• Centrality and structural metrics: Measure degree, betweenness, closeness, eigenvector centrality, PageRank, density, clustering, assortativity, and connectedness for interpreting roles in a network.
• Community detection and core-periphery structure: Identify clusters (Louvain/Leiden) and determine core-periphery structure (k-core / coreness) to understand group formation and influence asymmetries.
• ERGM-oriented structural inference: Support modeling how observed network structure arises using ERGM concepts (e.g., edges, reciprocity, transitivity) and report-ready workflow guidance.
• Network visualization and reporting: Produce publication-oriented network plots and use a checklist to ensure boundary specification, metric reporting, and sensitivity transparency.

Quick Start

Use the social-network-analysis skill to analyze an edge-list dataset by computing centralities, detecting communities, summarizing network statistics, and generating a network visualization for reporting.