Agent skill · data analytics · aj-geddes
Network Analysis
Analyze network structures, identify communities, measure centrality, and visualize relationships for social networks and organizational structures
Why this skill is useful
Provides specific Python scripts for calculating various centrality measures and visualizing network structures that are not commonly known.
What it needs
Requires matplotlib, networkx, numpy, pandas, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Network Analysis Overview This skill enables analysis of network structures to identify communities, measure centrality, detect influential nodes, and visualize complex relationships in social networks, organizational structures, and interconnected systems. When to Use Analyzing social networks to identify influential users and community structures Mapping organizational hierarchies and identifying key connectors or bottlenecks Studying citation networks to find impactful research papers and collaboration patterns Building recommendation systems based on network relationships and similarities Analyzing supply chain networks to optimize logistics and identify vulnerabilities Detecting fraud patterns through network analysis of financial transactions Network Concepts Nodes: Individual entities Edges: Connections/relationships Degree: Number of connections Centrality: Node importance measures Community: Densely connected groups Clustering Coefficient: Local density Key Metrics Degree Centrality: Number of connections Betweenness Centrality: Control over paths Closeness Centrality: Average distance to others Eigenvector Centrality: Connections to important nodes Modularity: Community structure strength Implementation with Python Centrality Measures Degree: Direct connections only Betweenness: Bridges between groups Closeness: Access to network Eigenvector: Connected to important nodes PageRank: Random walk probability Community Detection Modularity Optimization: Find dense groups Louvain Algorithm: Hierarchical communities K-clique: Overlapping communities Spectral: Eigenvalue-based Applications Social network analysis Organizational structures Citation networks Recommendation networks Supply chain analysis Deliverables Network visualization Centrality analysis Community detection results Connectivity metrics Influence rankings Key node identification Network statistics summary
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/network-analysis