performing-network-computation

Community

Compute network metrics and communities end-to-end.

Authorptreezh
Version1.0.0
Installs0

System Documentation

What problem does it solve?

When your social science data is stored as relationships, the task of turning it into a usable network and calculating meaningful statistics (indicators, centrality, and community structure) is time-consuming and error-prone.

Core Features & Use Cases

  • Network construction: Convert edge lists, adjacency matrices, or survey-style connection records into a network model, supporting directed/undirected and weighted graphs.
  • Network indicator computation: Calculate network size, density, path-based measures (e.g., diameter/radius/shortest paths), clustering, and connectivity components.
  • Advanced analytics: Run centrality measures (degree, closeness, betweenness, eigenvector, plus Katz and PageRank/HITS), detect communities (Louvain-style modularity optimization, label propagation, hierarchical/spectral-style approaches), and produce visualization outputs for reporting.

Quick Start

Use the performing-network-computation skill to compute indicators, centralities, and community structure for your social network dataset provided as an edge list.

Dependency Matrix

Required Modules

networkxnumpymatplotlibpython-louvainpython-louvain-community

Components

scripts

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: performing-network-computation
Download link: https://github.com/ptreezh/sscisubagent-skills/archive/main.zip#performing-network-computation

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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