---
name: network-analysis-94a988f72d86
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
graph:
  domains: [domain:social-sciences]
  skillAreas: [skill-area:graph-databases, skill-area:data-analysis, skill-area:data-visualization]
  workflows: [workflow:experiment-design, workflow:peer-review-cycle]
  roles: [role:data-scientist, role:analytics-engineer]
---

# 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


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/social-sciences-humanities/social-sciences/skills/network-analysis/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

# MIT License

Copyright (c) 2025 A5C AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/social-sciences-humanities/social-sciences/skills/network-analysis/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Marketing
Roles: Data Analyst, Growth Marketer, Market Research Analyst, Researcher, Social Media Manager
Specialties: Specialty not specified
Tools: Gephi, Igraph, Ucinet
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 2bd11bb6e213b1395c040891d88be032e076729436758a869704939edd860c69