How do I install the Networkx skill?

Run npx skillmds@latest add eyadsibai/networkx in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Networkx safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Networkx?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Networkx free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Networkx?

eyadsibai (@eyadsibai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Networkx by eyadsibai — Agent Skill

Name: Networkx
Rating: 4.2 (5 reviews)
Author: eyadsibai

File contents

NetworkX Graph Analysis

Python library for creating, analyzing, and visualizing networks and graphs.

When to Use

Social network analysis
Knowledge graphs and ontologies
Shortest path problems
Community detection
Citation/reference networks
Biological networks (protein interactions)

Graph Types

Type	Edges	Multiple Edges
`Graph`	Undirected	No
`DiGraph`	Directed	No
`MultiGraph`	Undirected	Yes
`MultiDiGraph`	Directed	Yes

Key Algorithms

Centrality Measures

Measure	What It Finds	Use Case
Degree	Most connections	Popular nodes
Betweenness	Bridge nodes	Information flow
Closeness	Fastest reach	Efficient spreaders
PageRank	Importance	Web pages, citations
Eigenvector	Influential connections	Who knows important people

Path Algorithms

Algorithm	Purpose
Shortest path	Minimum hops
Weighted shortest	Minimum cost
All pairs shortest	Full distance matrix
Dijkstra	Efficient weighted paths

Community Detection

Method	Approach
Louvain	Modularity optimization
Greedy modularity	Hierarchical merging
Label propagation	Fast, scalable

Graph Generators

Generator	Model
Erdős-Rényi	Random edges
Barabási-Albert	Preferential attachment (scale-free)
Watts-Strogatz	Small-world
Complete	All connected

Layout Algorithms

Layout	Best For
Spring	General purpose
Circular	Regular structure
Kamada-Kawai	Aesthetics
Spectral	Clustered graphs

I/O Formats

Format	Preserves Attributes	Human Readable
GraphML	Yes	Yes (XML)
Edge list	No	Yes
JSON	Yes	Yes
Pandas	Yes	Via DataFrame

Performance Considerations

Scale	Approach
< 10K nodes	Any algorithm
10K - 100K	Use approximate algorithms
> 100K	Consider graph-tool or igraph

Key concept: NetworkX is pure Python - great for prototyping, may need alternatives for production scale.

Best Practices

Set random seeds for reproducibility
Choose correct graph type upfront
Use pandas integration for data exchange
Consider memory for large graphs

Resources

NetworkX docs: https://networkx.org/documentation/latest/
Tutorial: https://networkx.org/documentation/latest/tutorial.html

---name: networkxdescription: Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph"--- # NetworkX Graph Analysis Python library for creating, analyzing, and visualizing networks and graphs. ## When to Use - Social network analysis - Knowledge graphs and ontologies - Shortest path problems - Community detection - Citation/reference networks - Biological networks (protein interactions) --- ## Graph Types | Type | Edges | Multiple Edges | |------|-------|----------------| | `Graph` | Undirected | No | | `DiGraph` | Directed | No | | `MultiGraph` | Undirected | Yes | | `MultiDiGraph` | Directed | Yes | --- ## Key Algorithms ### Centrality Measures | Measure | What It Finds | Use Case | |---------|---------------|----------| | **Degree** | Most connections | Popular nodes | | **Betweenness** | Bridge nodes | Information flow | | **Closeness** | Fastest reach | Efficient spreaders | | **PageRank** | Importance | Web pages, citations | | **Eigenvector** | Influential connections | Who knows important people | ### Path Algorithms | Algorithm | Purpose | |-----------|---------| | **Shortest path** | Minimum hops | | **Weighted shortest** | Minimum cost | | **All pairs shortest** | Full distance matrix | | **Dijkstra** | Efficient weighted paths | ### Community Detection | Method | Approach | |--------|----------| | **Louvain** | Modularity optimization | | **Greedy modularity** | Hierarchical merging | | **Label propagation** | Fast, scalable | --- ## Graph Generators | Generator | Model | |-----------|-------| | **Erdős-Rényi** | Random edges | | **Barabási-Albert** | Preferential attachment (scale-free) | | **Watts-Strogatz** | Small-world | | **Complete** | All connected | --- ## Layout Algorithms | Layout | Best For | |--------|----------| | **Spring** | General purpose | | **Circular** | Regular structure | | **Kamada-Kawai** | Aesthetics | | **Spectral** | Clustered graphs | --- ## I/O Formats | Format | Preserves Attributes | Human Readable | |--------|---------------------|----------------| | **GraphML** | Yes | Yes (XML) | | **Edge list** | No | Yes | | **JSON** | Yes | Yes | | **Pandas** | Yes | Via DataFrame | --- ## Performance Considerations | Scale | Approach | |-------|----------| | < 10K nodes | Any algorithm | | 10K - 100K | Use approximate algorithms | | > 100K | Consider graph-tool or igraph | **Key concept**: NetworkX is pure Python - great for prototyping, may need alternatives for production scale. --- ## Best Practices - Set random seeds for reproducibility - Choose correct graph type upfront - Use pandas integration for data exchange - Consider memory for large graphs ## Resources - NetworkX docs: <https://networkx.org/documentation/latest/> - Tutorial: <https://networkx.org/documentation/latest/tutorial.html>

Frequently asked questions

Networkx