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sna-analysis

Construct relationship graphs and compute network metrics for social-science datasets.

24|7|Updated Nov 15, 2025
Search Tags:#network visualization#centrality#community detection#social network analysis#graph analytics#social science research#structural holes
One-click install
npx skills add https://github.com/ptreezh/sscisubagent-skills --skill sna-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sna-analysis
Source: https://github.com/ptreezh/sscisubagent-skills/tree/main/skills/sna-analysis
Command: npx skills add https://github.com/ptreezh/sscisubagent-skills --skill sna-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Social network research often suffers from unclear structure and weak evidence, making it hard to identify key actors, hidden communities, and bridging roles in relational datasets.

Core Features & Use Cases

  • Network construction: Build nodes and edges from relation datasets to create an analyzable network.
  • Centrality analysis: Identify influential or critical actors using standard network metrics.
  • Community detection & structural hole analysis: Detect clusters and reveal bridging positions that connect otherwise separated groups.
  • Network visualization: Produce interpretable visual outputs to communicate findings to others.
  • Use Case: For example, a researcher can analyze collaboration relationships among organizations to find the most central institutions, uncover sub-communities, and locate structural holes that indicate potential brokerage opportunities.

Quick Start

Use the sna-analysis skill to analyze the provided social network edges and return the main centrality results, detected communities, structural hole candidates, and a visualization summary.

Frequently Asked Questions about sna-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify key actors and hidden communities in social network data?▼

Social network analysis builds relationship graphs from your dataset and computes network metrics to reveal influential actors and detect hidden communities. It processes relational data to uncover structural patterns and bridging roles within the network.

What is structural hole analysis and when do I need it for social science research?▼

Structural hole analysis identifies bridging positions that connect otherwise separated groups within a network. You need it when researching brokerage opportunities or understanding how information flows between unconnected communities in social science datasets.

How do I perform centrality ranking and community detection on relationship datasets?▼

Construct a network graph from your nodes and edges, then apply centrality metrics to rank influential actors and run community detection algorithms to identify clusters. The analysis outputs centrality scores, community assignments, and structural hole candidates.

Can I use Python and bash to generate network visualization outputs from edge lists?▼

Yes, this analysis operates with Python and bash tooling alongside file read/write capabilities to process edge lists and generate interpretable network visualization outputs. It requires intermediate analytical tooling support to produce visual summaries.

What is the best way to analyze collaboration relationships among organizations?▼

Build nodes and edges from the collaboration dataset, compute standard network centrality metrics to find central institutions, and run community detection to uncover sub-communities. This reveals brokerage opportunities and structural holes connecting separated groups.

What are the limitations of social network analysis for relational datasets?▼

Social network analysis requires structured relationship data with clear nodes and edges to function effectively. It needs intermediate analytical tooling support and cannot interpret unstructured text or derive relationships without pre-built edge lists.

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