Agent skill · research science · k-dense-ai
networkx
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.
Why this skill is useful
Provides domain-specific knowledge on graph algorithms and network generation techniques that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, networkx installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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.writegpickle, nx.readshp) and the 3.4-era nx.randomtree 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 Network comparison: Checking isomorphism, computing graph metrics, analyzing structural properties Core Capabilities 1. Graph Creation and Manipulation NetworkX supports four main graph types: Graph: Undirected graphs with single edges DiGraph: Directed graphs with one-way connections MultiGraph: Undirected graphs allowing multiple edges between nodes MultiDiGraph: Directed graphs with multiple edges Create graphs by: Reference: See references/graph-basics.md for comprehensive guidance on creating, modifying, examining, and managing graph structures, including working with attributes and subgraphs. 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/networkx