skills.rest
dralkh
dralkhCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

networkx

Build, analyze, and visualize NetworkX graphs with algorithms and file I/O.

74|5|Updated Dec 10, 2025
Search Tags:#graph analysis#graph visualization#networkx#centrality#community detection#shortest paths#graph io
One-click install
npx skills add https://github.com/dralkh/seerai --skill networkx-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/dralkh/seerai/tree/main/skills/networkx
Command: npx skills add https://github.com/dralkh/seerai --skill networkx-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Network analysis often requires the same repeated steps: building graphs, computing structural metrics, generating test networks, loading data from files, and turning results into clear visuals. This Skill streamlines that workflow for Python users working with complex relationships across research, operations, and engineering problems.

Core Features & Use Cases

  • Graph creation and manipulation: Create directed, undirected, and multigraph structures with attributes.
  • Algorithms and analysis: Compute shortest paths, centrality, clustering, connectivity, flows, cycles, cliques, matching, isomorphism, and traversal results.
  • Generation, I/O, and visualization: Produce synthetic networks, read and write common graph formats, and draw publication-ready network diagrams.
  • Use case: A researcher can load citation data, detect communities, measure influence, export the network to GraphML, and generate a labeled visualization for a paper or report.

Quick Start

Ask the skill to create, analyze, and visualize your NetworkX graph from the provided data and return the Python code and results you need.

Frequently Asked Questions about networkx

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

FAQPage Schema
How do I compute centrality and detect communities in a graph using Python?▼

Graph analysis in Python lets you compute centrality and detect communities by building directed or undirected graph structures with attributes, then applying algorithm modules to measure influence and identify clustered subgroups within the network.

What's the best way to visualize a network graph for a publication or report?▼

Network visualization for publications involves generating reproducible layouts with seeded randomness to produce clear, labeled diagrams, turning complex graph relationship data into readable network maps suitable for research papers.

Can I import and export graph data in common file formats like GraphML?▼

Graph I/O supports reading and writing common graph formats including GraphML, enabling you to load external network data, process it, and export the analyzed structural results for interoperability with other tools.

Does Python graph analysis work for social, biological, and transportation networks?▼

Python graph analysis supports social, biological, transportation, citation, and knowledge-graph workflows, allowing you to model diverse complex relationships and compute structural metrics like shortest paths and connectivity across different network domains.

How do I generate synthetic networks for testing graph algorithms?▼

Synthetic network generation produces test networks using reproducible generators with seeded randomness, creating controlled graph structures to validate algorithm behavior and structural metrics before applying them to real network data.

Related Skills

Explore More Data & Analytics Skills→
cocyuhaococyuhao
1

json-canvas

Create and validate JSON Canvas files with nodes and edges per Spec 1.0.

Community
Intermediate
sagunkayasthasagunkayastha

networkx

Create, manipulate, analyze, and visualize complex networks and graphs in Python.

Community
Advanced
hxk622hxk622
8

matlab

Execute MATLAB and GNU Octave scripts for numerical computing and visualization.

Community
Advanced
hung-phanhung-phan

graph-db

Analyze graph database designs and optimize Cypher, Gremlin, and SPARQL queries.

Community
Advanced