Networkx

If you're working with any kind of network or graph data in Python, this is the standard toolkit. It handles everything from social networks to biological pathways to citation graphs. You get the four main graph types (directed, undirected, multi-edge variants), a comprehensive set of algorithms for centrality, shortest paths, community detection, and graph generators for creating synthetic networks when you need test data. The pandas and numpy integration is solid, so you can go from DataFrames to graphs without friction. Visualization works through matplotlib with various layout algorithms, though for production dashboards you'll want to export to something more interactive. The API is intuitive and the 3-clause BSD license means you can use it anywhere.

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last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill networkx --agent claude-code

Installs into .claude/skills of the current project.

Files

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 tested NetworkX 3.7 (Python >=3.12, excluding 3.14.1). 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.

Review sources, executed examples, optional-dependency limits, and reproducibility notes are in references/review.md. Reference snippets are API patterns: supply the stated graph type and input files; they are not one sequential script.