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NetworkX

Maintainer K-Dense Inc. · Last updated April 1, 2026

NetworkX is a Python package for creating, manipulating, and analy.

Claude CodeOpenClawNanoClawAnalysisWritingnetworkxdata-analysispackagedata analysis & visualization

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/networkx

Maintainer: K-Dense Inc.
License: 3-clause BSD license
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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.
G.add_node(1) G.add_nodes_from([2, 3, 4]) G.add_node("protein_A", type='enzyme', weight=1.5).

Source Doc

Excerpt From SKILL.md

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

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:

import networkx as nx

## Add edges

G.add_edge(1, 2)
G.add_edges_from([(1, 3), (2, 4)])
G.add_edge(1, 4, weight=0.8, relation='interacts')

Reference: See references/graph-basics.md for comprehensive guidance on creating, modifying, examining, and managing graph structures, including working with attributes and subgraphs.

Use cases

**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.

Not for

Do not rely on this catalog entry alone for installation or maintenance details.
Do not treat this catalog entry as a substitute for the full upstream workflow.