performing-centrality-analysis

What problem does it solve?

This Skill helps you quantify and interpret key nodes in a social network so you can understand power centers, information bottlenecks, and influence structure rather than relying on intuition.

Core Features & Use Cases

• Four centrality metrics: computes degree, closeness, betweenness, and eigenvector centrality to capture different notions of “importance”.
• Key-node identification: classifies hubs (degree), bridges (betweenness), and influencers (eigenvector) using threshold rules.
• Research-context interpretation: supports Chinese social-network framing (e.g., guanxi/relationships) to help explain what the numbers mean in practice.

Use case: You have a network of actors in an organization or collaboration graph and need to identify which individuals are central connectors, fast information spreaders, and influential figures for a research write-up.

Quick Start

Run the centrality pipeline by telling an AI: compute all four centrality measures for network.json and output centrality.json with the top 20 nodes for each chosen metric.