Networkx

---
name: networkx
description: Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
license: 3-clause BSD license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---

# NetworkX

## Overview

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities.

This skill targets NetworkX 3.x (current stable: 3.6, which requires Python >= 3.11). Several pre-3.0 APIs (`nx.info`, `nx.write_gpickle`, `nx.read_shp`) and the 3.4-era `nx.random_tree` no longer exist — current replacements are used throughout this skill.

## When to Use This Skill

Invoke this skill when tasks involve:

- **Creating graphs**: Building network structures from data, adding nodes and edges with attributes
- **Graph analysis**: Computing centrality measures, finding shortest paths, detecting communities, measuring clustering
- **Graph algorithms**: Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow
- **Network generation**: Creating synthetic networks (random, scale-free, small-world models) for testing or simulation
- **Graph I/O**: Reading from or writing to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices)
- **Visualization**: Drawing and customizing network visualizations with matplotlib or interactive libraries… load the full skill through Skill Me