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FrancisVarga
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Graph Algorithms

Execute PageRank, Louvain, BFS, DFS, and shortest path algorithms on in-memory property graphs.

Updated Feb 13, 2026
Search Tags:#graph analysis#traversal#pagerank#graph algorithms#louvain#community detection
One-click install
npx skills add https://github.com/FrancisVarga/stupid-db --skill graph-algorithms
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Graph Algorithms
Source: https://github.com/FrancisVarga/stupid-db/tree/main/packages/stupid-claude-agent/.claude/skills/graph-algorithms
Command: npx skills add https://github.com/FrancisVarga/stupid-db --skill graph-algorithms

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a suite of powerful graph algorithms to analyze and understand complex relationships within the in-memory property graph of stupid-db, enabling the discovery of influential entities, community structures, and connection paths.

Core Features & Use Cases

  • Influence Analysis: Identify key nodes using PageRank.
  • Community Detection: Discover natural groupings of nodes with Louvain.
  • Pathfinding & Exploration: Perform BFS, DFS, and shortest path traversals.
  • Graph Health Metrics: Generate statistics for monitoring and LLM insights.
  • Use Case: Analyze a social network graph to find the most influential users (PageRank) or to identify distinct user communities (Louvain).

Quick Start

Use the graph algorithms skill to find the top 10 most influential nodes in the graph using PageRank.

Frequently Asked Questions about Graph Algorithms

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

FAQPage Schema
How do I find the most influential nodes in a social network graph?▼

To find the most influential nodes in a social network graph, execute the PageRank algorithm on your in-memory property graph, which calculates and ranks node influence based on connection structures.

What is the best way to detect communities within a large network graph?▼

The best way to detect communities within a large network graph is using the Louvain algorithm, which identifies natural groupings of interconnected entities and supports parallel computation for large datasets.

Can I perform BFS and DFS traversals to find connection paths in an in-memory property graph?▼

Yes, you can perform BFS, DFS, and shortest path traversals directly on an in-memory property graph to explore connection paths and identify interconnected entities within your network.

Do I need a specific graph structure to run advanced graph algorithms for influence analysis?▼

Yes, you need a GraphStore object containing nodes, edges, and adjacency information to execute advanced graph algorithms for influence analysis and community detection.

How do I generate graph health metrics for monitoring network structures?▼

You generate graph health metrics for monitoring network structures by running statistics generation on the in-memory property graph, producing data insights suitable for monitoring and LLM analysis.

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