network-analysis-math
CommunityAnalyze graphs: centrality and communities
Education & Research#graph theory#network analysis#networkx#centrality#pagerank#community detection#leiden algorithm
Authorxjtulyc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill helps you construct and analyze complex networks so you can measure node importance, find communities, and compare real networks against common random graph models.
Core Features & Use Cases
- Graph construction & preprocessing: Build graphs from edge lists, adjacency matrices, or pandas edge tables using networkx.
- Network measurements: Compute centrality metrics like degree, betweenness, closeness, PageRank, and eigenvector centrality, plus summary statistics (density, clustering, paths, diameter).
- Community detection: Detect clusters with modularity optimization using Louvain/Leiden-style workflows via igraph + leidenalg.
- Modeling & robustness-ready analysis: Generate and compare random graph models (Erdős-Rényi, Barabási–Albert, Watts–Strogatz) and support downstream studies like robustness and spreading.
Quick Start
Use this Skill to analyze a CSV-like edge list by loading it into a network graph, computing PageRank and betweenness, and running Leiden community detection to label nodes by cluster.
Dependency Matrix
Required Modules
networkxpython-igraphleidenalgmatplotlibnumpypandasscipy
Components
Standard package💻 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: network-analysis-math Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#network-analysis-math Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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