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weighted-brain-community-detection

Detect multi-scale community structure in weighted brain networks using Asymptotical Surprise.

2|Updated Feb 12, 2026
Search Tags:#neuroscience#community-detection#multiscale#fMRI#brain-network#weighted-connectivity#asymptotical-surprise
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill weighted-brain-community-detection
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weighted-brain-community-detection
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/weighted-brain-community-detection
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill weighted-brain-community-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Detects multi-scale community structure in weighted brain connectivity networks beyond the resolution limit using Asymptotical Surprise.

Core Features & Use Cases

  • Detects modular organization across scales in weighted brain networks.
  • Applies to resting-state fMRI and other brain connectivity datasets to compare modular patterns across subjects or conditions.
  • Provides a principled framework for exploring brain network organization beyond fixed-resolution methods.

Quick Start

Run the Asymptotical Surprise based analysis on your weighted brain connectivity data to obtain multi-scale modules.

Frequently Asked Questions about weighted-brain-community-detection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect multi-scale community structure in weighted brain networks?▼

Detect multi-scale community structure in weighted brain networks by applying continuous Asymptotical Surprise optimization to reveal hierarchical modular organization across resting-state fMRI datasets.

What is Asymptotical Surprise optimization for brain network analysis?▼

Asymptotical Surprise is a continuous optimization method used to detect multi-scale community structure in weighted brain connectivity networks, revealing modular partitions beyond the resolution limit.

Can I use this method to analyze resting-state fMRI connectivity data?▼

Yes, you can apply this to resting-state fMRI and other brain connectivity datasets to compare modular patterns across subjects or conditions and reveal hierarchical modular organization.

Why does my brain network community detection miss small modules?▼

Fixed-resolution community detection methods hit a resolution limit and miss small modules. Using Asymptotical Surprise optimization uncovers multi-scale brain modules beyond this limit.

What's the best way to compare modular patterns across fMRI subjects?▼

Run Asymptotical Surprise based analysis on weighted brain connectivity data to obtain multi-scale module partitions, enabling comparison of modular patterns across subjects or conditions.

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