Louvain Community Detection

Louvain Community Detection is a Data skill for Claude Code, published by asgard-ai-platform in skills.

228 stars28 forkson asgard-ai-platform/skillsAdded 2026/09/03Repository updated 2026/06/06

ai-agentanthropicclaudeclaude-agent-skillsclaude-codecoding-agentknowledge-basemcpmethodologyopen-sourceprompt-engineeringskillstaiwan

Install in seconds

Install Louvain Community Detection

Copy Louvain Community Detection into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-net-community ~/.claude/skills/algo-net-community

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/asgard-ai-platform/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: algo-net-community/SKILL.md in asgard-ai-platform/skills
Installs to: ~/.claude/skills/algo-net-community
Collection: One of 25 skills cataloged from this repository
Category: Data — 710 skills

What Louvain Community Detection does

Louvain Community Detection finds densely connected groups in weighted graphs by optimizing modularity. Use it to segment social, organizational, or customer networks and to analyze network structure.

Louvain Community Detection is cataloged under Data on DirSkills. Louvain Community Detection comes from a repository tagged ai-agent, anthropic, claude, claude-agent-skills and claude-code.

Documentation

README

Excerpt

Louvain Community Detection

Overview

Louvain algorithm detects communities by optimizing modularity — the fraction of edges within communities minus expected fraction if edges were random. A greedy, hierarchical algorithm that runs in O(n log n) for sparse graphs. Produces a hierarchy of communities at multiple resolutions.

When to Use

Trigger conditions:

Discovering natural groupings in social, organizational, or interaction networks
Segmenting users/customers by behavioral similarity
Analyzing modular structure of complex networks

When NOT to use:

For overlapping communities (use DEMON or BigCLAM)
When communities are pre-defined and you're classifying nodes (use label propagation)

Algorithm

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Louvain Community Detection

What else does asgard-ai-platform publish alongside Louvain Community Detection?
Louvain Community Detection is one of 25 skills that DirSkills catalogs from asgard-ai-platform/skills, the repository it ships in. Its siblings there include ARIMA Time Series Model, Ad Bidding Strategies and Ad Budget Allocation. Each one is a separate skill with its own page in this directory, installs the same way Louvain Community Detection does, and is maintained by asgard-ai-platform in that same repository. The rest of the collection is listed on the asgard-ai-platform/skills page.
How does Louvain Community Detection compare to other Data skills?
Louvain Community Detection ranks #647 by stars among the 710 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Louvain Community Detection against them. Open each page to compare what they document and how they install.