Skills networkx

🔗

networkx

Name: networkx
Author: K-Dense-AI

Safe ⚙️ External commands📁 Filesystem access🌐 Network access

Analyze and visualize complex networks with NetworkX

Also available from: davila7

Working with graph and network data requires specialized tools for creating structures, computing algorithms, and generating visualizations. This skill provides comprehensive access to NetworkX capabilities for social network analysis, biological pathway mapping, transportation optimization, and relationship modeling.

Supports: Claude Codex Code(CC)

📊 71 Adequate

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Test it

Using "networkx". Create a graph showing connections between team members

Expected outcome:

Graph created with 8 nodes representing team members
Edges added for direct reporting relationships
Nodes labeled with team member names
Drawn with circular layout for clarity

Using "networkx". Find the most central nodes in a citation network

Expected outcome:

Betweenness centrality computed for all 50 nodes
Top 5 most central papers identified with scores
Results saved to centrality_results.csv
Visualization shows node size by centrality value

Security Audit

Safe

v4 • 1/17/2026

This is a documentation-only skill containing markdown files for the NetworkX Python library. All 495 static findings are FALSE POSITIVEs. The analyzer misinterprets markdown code fences as shell execution, NetworkX function names as cryptographic algorithms, and graph operations as system reconnaissance. No executable code exists. All URLs point to official NetworkX documentation.

Files scanned

4,486

Lines analyzed

findings

Total audits

Risk Factors

⚙️ External commands (349)

📁 Filesystem access (7)

references/io.md:210 references/io.md:223 references/io.md:236 references/io.md:252 references/io.md:278 references/io.md:327 references/io.md:396

🌐 Network access (5)

skill-report.json:6 SKILL.md:431 SKILL.md:432 SKILL.md:433 SKILL.md:434

Audited by: claude View Audit History →

Quality Score

Architecture

100

Maintainability

Content

Community

100

Security

Spec Compliance

What You Can Build

Social network analysis

Analyze relationship patterns, identify influencers, and detect communities in social media data

Biological pathway mapping

Model protein interactions, gene regulatory networks, and metabolic pathways

Transportation optimization

Optimize route planning, supply chain networks, and infrastructure design

Try These Prompts

Basic graph creation

Create a graph with 5 nodes and add edges between them to represent a basic network structure

Path finding

Find the shortest path between node A and node F in this graph and show the distance

Centrality analysis

Compute betweenness centrality for all nodes and identify which nodes are most critical

Network generation

Generate a Barabasi-Albert scale-free network with 100 nodes and 3 edges per new node

Best Practices

Set random seeds (seed=42) for reproducible random graph generation and force-directed layouts
Use sparse matrix formats and appropriate data structures when working with large networks
Save graphs in GraphML format to preserve node and edge attributes

Avoid

Using floating-point numbers as node identifiers can cause precision issues in algorithms
Loading entire large networks when only subgraph analysis is needed wastes memory
Forgetting to check graph connectivity before running algorithms that require connected graphs

Frequently Asked Questions

What graph types does NetworkX support?

NetworkX supports Graph (undirected), DiGraph (directed), MultiGraph (multiple edges undirected), and MultiDiGraph (multiple edges directed).

How do I read a graph from a CSV file?

Use pandas to read the CSV, then nx.from_pandas_edgelist() with source, target, and optional edge attribute columns.

What is the fastest way to find shortest paths?

Use nx.shortest_path() for single pairs. For all pairs, use nx.all_pairs_shortest_path() with an iterator to save memory.

Can NetworkX handle directed acyclic graphs?

Yes, NetworkX handles DAGs with functions like nx.is_directed_acyclic_graph() and topological sorting via nx.topological_sort().

How do I detect communities in a network?

Use community.greedy_modularity_communities(G) for modularity-based detection or other algorithms in networkx.algorithms.community.

What visualization libraries work with NetworkX?

Matplotlib integrates natively. For interactive visualizations, use PyVis or Plotly with NetworkX graph objects.

Developer Details

Author

K-Dense-AI

License

3-clause BSD license

Repository

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

Ref

main

File structure

📁 references/

📄 algorithms.md

📄 generators.md

📄 graph-basics.md

📄 io.md

📄 visualization.md

📄 SKILL.md