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graph-analytics

Analyze RDF graphs with PageRank, degree centrality, and label propagation.

8|1|Updated Jun 7, 2026
Search Tags:#sparql#rdf#community detection#graph analytics#page rank
One-click install
npx skills add https://github.com/sparq-org/sparq --skill graph-analytics-sparq-org
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graph-analytics
Source: https://github.com/sparq-org/sparq/tree/main/skills/graph-analytics
Command: npx skills add https://github.com/sparq-org/sparq --skill graph-analytics-sparq-org

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill enables users to perform sophisticated graph analytics over RDF data, extracting insights such as importance ranking, community detection, and connected components without requiring additional models or networks.

Core Features & Use Cases

  • Entity Importance: Use PageRank and degree centrality to determine the importance of entities within a graph.
  • Community Detection: Apply label propagation to find communities and weakly-connected components.
  • Connected Components: Compute connected components using union-find algorithms.
  • Use Case: If you are analyzing a social network's RDF graph and want to understand the importance of individuals and their connections, this Skill can help you identify influential nodes and community structures.

Quick Start

To analyze a sparq RDF graph, project the graph onto a directed node view using 'sparq-algos' and run algorithms like PageRank or degree centrality.

Frequently Asked Questions about graph-analytics

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

FAQPage Schema
How do I run PageRank on RDF data to find important entities?▼

You can compute entity importance on RDF data by projecting the graph onto a directed node view and applying PageRank. This calculates node importance based on incoming link structures within the dataset.

What is the best way to detect communities in an RDF graph?▼

Community detection in an RDF graph is performed using label propagation algorithms. This technique assigns labels to nodes and iteratively updates them based on neighbors, revealing underlying community structures and densely connected groups.

Can I compute connected components on RDF graphs without external dependencies?▼

Yes, you can compute connected components on RDF graphs without external dependencies. The Skill operates directly on RDF data from sparq_core::Graph, utilizing union-find algorithms to identify weakly-connected components independently.

How does degree centrality work for analyzing social network RDF graphs?▼

Degree centrality analyzes social network RDF graphs by counting the number of direct connections for each node. This metric identifies highly connected entities, helping you understand influence and connectivity patterns within the network.

Do I need additional models to perform graph analytics on RDF data?▼

No, you do not need additional models or networks to perform graph analytics on RDF data. The Skill computes graph properties such as node importance and connected components directly using built-in graph algorithms.

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