# Networkx

> Crea, analiza y visualiza redes y grafos complejos en Python con NetworkX: algoritmos de grafos (caminos más cortos, centralidad, clustering), detección de comunidades, generación de redes sintéticas y E/S de formatos.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/networkx
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/networkx.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: 3-clause BSD license
Actualizado: el mes pasado
Coste de contexto: 112 tok instalada, 3.4k tok al activarse, 15.2k tok con todos los archivos del bundle
Bundle: 6 archivos, 59 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent cline
```

## Qué hace

- Crea y manipula grafos (Graph, DiGraph, MultiGraph, MultiDiGraph) con nodos y aristas con atributos
- Calcula algoritmos de grafos: caminos más cortos, centralidad (grado, intermediación, PageRank), clustering y componentes conexas
- Detecta comunidades con algoritmos como greedy_modularity_communities
- Genera redes sintéticas (Erdős-Rényi, Barabási-Albert, Watts-Strogatz) y grafos clásicos o estructurados
- Lee y escribe grafos en formatos como edge list, GraphML, GML, JSON, CSV y matrices, y los dibuja con matplotlib

## Cuándo usarla

- Se trabaja con estructuras de datos de red o grafo (sociales, biológicas, de transporte, de citas)
- Se calculan algoritmos como Dijkstra, PageRank, árboles de expansión mínima o flujo máximo
- Se generan redes sintéticas para pruebas o simulación
- Se leen o escriben grafos en distintos formatos, o se visualizan topologías de red

## Qué la activa

- "Calcula la centralidad de intermediación de este grafo con NetworkX"
- "Detecta comunidades en esta red social"
- "Genera una red Barabási-Albert de 100 nodos"
- "Dibuja este grafo con un layout de resorte"

## Antes de instalar

- Está pensado para NetworkX 3.x (estable 3.6), que requiere Python >= 3.11; se instala con `uv pip install networkx`.

## Archivos

- SKILL.md — 13 KB
- references/algorithms.md — 9 KB
- references/generators.md — 8 KB
- references/graph-basics.md — 6 KB
- references/io.md — 10 KB
- references/visualization.md — 12 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo 3-clause BSD license. Esta sección es el documento original y está en inglés.

# 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.write_gpickle`, `nx.read_shp`) and the 3.4-era `nx.random_tree` 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:
```python
import networkx as nx

# Create empty graph
G = nx.Graph()

# Add nodes (can be any hashable type)
G.add_node(1)
G.add_nodes_from([2, 3, 4])
G.add_node("protein_A", type='enzyme', weight=1.5)

# 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.

### 2. Graph Algorithms

NetworkX provides extensive algorithms for network analysis:

**Shortest Paths**:
```python
# Find shortest path
path = nx.shortest_path(G, source=1, target=5)
length = nx.shortest_path_length(G, source=1, target=5, weight='weight')
```

**Centrality Measures**:
```python
# Degree centrality
degree_cent = nx.degree_centrality(G)

# Betweenness centrality
betweenness = nx.betweenness_centrality(G)

# PageRank
pagerank = nx.pagerank(G)
```

**Community Detection**:
```python
from networkx.algorithms import community

# Detect communities
communities = community.greedy_modularity_communities(G)
```

**Connectivity**:
```python
# Check connectivity
is_connected = nx.is_connected(G)

# Find connected components
components = list(nx.connected_components(G))
```

**Reference**: See `references/algorithms.md` for detailed documentation on all available algorithms including shortest paths, centrality measures, clustering, community detection, flows, matching, tree algorithms, and graph traversal.

### 3. Graph Generators

Create synthetic networks for testing, simulation, or modeling:

**Classic Graphs**:
```python
# Complete graph
G = nx.complete_graph(n=10)

# Cycle graph
G = nx.cycle_graph(n=20)

# Known graphs
G = nx.karate_club_graph()
G = nx.petersen_graph()
```

**Random Networks**:
```python
# Erdős-Rényi random graph
G = nx.erdos_renyi_graph(n=100, p=0.1, seed=42)

# Barabási-Albert scale-free network
G = nx.barabasi_albert_graph(n=100, m=3, seed=42)

# Watts-Strogatz small-world network
G = nx.watts_strogatz_graph(n=100, k=6, p=0.1, seed=42)
```

**Structured Networks**:
```python
# Grid graph
G = nx.grid_2d_graph(m=5, n=7)

# Random tree (random_tree was removed in NetworkX 3.4)
G = nx.random_labeled_tree(100, seed=42)
```

**Reference**: See `references/generators.md` for comprehensive coverage of all graph generators including classic, random, lattice, bipartite, and specialized network models with detailed parameters and use cases.

### 4. Reading and Writing Graphs

NetworkX supports numerous file formats and data sources:

**File Formats**:
```python
# Edge list
G = nx.read_edgelist('graph.edgelist')
nx.write_edgelist(G, 'graph.edgelist')

# GraphML (preserves attributes)
G = nx.read_graphml('graph.graphml')
nx.write_graphml(G, 'graph.graphml')

# GML
G = nx.read_gml('graph.gml')
nx.write_gml(G, 'graph.gml')

# JSON (node-link format; edge list is stored under the "edges" key
# since NetworkX 3.6 — older files may use "links", see references/io.md)
data = nx.node_link_data(G)
G = nx.node_link_graph(data)
```

**Pandas Integration**:
```python
import pandas as pd

# From DataFrame
df = pd.DataFrame({'source': [1, 2, 3], 'target': [2, 3, 4], 'weight': [0.5, 1.0, 0.75]})
G = nx.from_pandas_edgelist(df, 'source', 'target', edge_attr='weight')

# To DataFrame
df = nx.to_pandas_edgelist(G)
```

**Matrix Formats**:
```python
import numpy as np

# Adjacency matrix
A = nx.to_numpy_array(G)
G = nx.from_numpy_array(A)

# Sparse matrix
A = nx.to_scipy_sparse_array(G)
G = nx.from_scipy_sparse_array(A)
```

**Reference**: See `references/io.md` for complete documentation on all I/O formats including CSV, SQL databases, Cytoscape, DOT, and guidance on format selection for different use cases.

### 5. Visualization

Create clear and informative network visualizations:

**Basic Visualization**:
```python
import matplotlib.pyplot as plt

# Simple draw
nx.draw(G, with_labels=True)
plt.show()

# With layout
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500)
plt.show()
```

**Customization**:
```python
# Color by degree
node_colors = [G.degree(n) for n in G.nodes()]
nx.draw(G, node_color=node_colors, cmap=plt.cm.viridis)

# Size by centrality
centrality = nx.betweenness_centrality(G)
node_sizes = [3000 * centrality[n] for n in G.nodes()]
nx.draw(G, node_size=node_sizes)

# Edge weights
edge_widths = [3 * G[u][v].get('weight', 1) for u, v in G.edges()]
nx.draw(G, width=edge_widths)
```

**Layout Algorithms**:
```python
# Spring layout (force-directed)
pos = nx.spring_layout(G, seed=42)

# Circular layout
pos = nx.circular_layout(G)

# Kamada-Kawai layout
pos = nx.kamada_kawai_layout(G)

# Spectral layout
pos = nx.spectral_layout(G)
```

**Publication Quality**:
```python
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, node_color='lightblue', node_size=500,
        edge_color='gray', with_labels=True, font_size=10)
plt.title('Network Visualization', fontsize=16)
plt.axis('off')
plt.tight_layout()
plt.savefig('network.png', dpi=300, bbox_inches='tight')
plt.savefig('network.pdf', bbox_inches='tight')  # Vector format
```

**Reference**: See `references/visualization.md` for extensive documentation on visualization techniques including layout algorithms, customization options, interactive visualizations with Plotly and PyVis, 3D networks, and publication-quality figure creation.

## Working with NetworkX

### Installation

Ensure NetworkX is installed:
```python
# Check if installed
import networkx as nx
print(nx.__version__)

# Install if needed (via bash)
# uv pip install networkx
# uv pip install networkx[default]  # With optional dependencies
```

### Common Workflow Pattern

Most NetworkX tasks follow this pattern:

1. **Create or Load Graph**:
   ```python
   # From scratch
   G = nx.Graph()
   G.add_edges_from([(1, 2), (2, 3), (3, 4)])

   # Or load from file/data
   G = nx.read_edgelist('data.txt')
   ```

2. **Examine Structure**:
   ```python
   print(f"Nodes: {G.number_of_nodes()}")
   print(f"Edges: {G.number_of_edges()}")
   print(f"Density: {nx.density(G)}")
   print(f"Connected: {nx.is_connected(G)}")
   ```

3. **Analyze**:
   ```python
   # Compute metrics
   degree_cent = nx.degree_centrality(G)
   avg_clustering = nx.average_clustering(G)

   # Find paths
   path = nx.shortest_path(G, source=1, target=4)

   # Detect communities
   communities = community.greedy_modularity_communities(G)
   ```

4. **Visualize**:
   ```python
   pos = nx.spring_layout(G, seed=42)
   nx.draw(G, pos=pos, with_labels=True)
   plt.show()
   ```

5. **Export Results**:
   ```python
   # Save graph
   nx.write_graphml(G, 'analyzed_network.graphml')

   # Save metrics
   df = pd.DataFrame({
       'node': list(degree_cent.keys()),
       'centrality': list(degree_cent.values())
   })
   df.to_csv('centrality_results.csv', index=False)
   ```

### Important Considerations

**Floating Point Precision**: When graphs contain floating-point numbers, all results are inherently approximate due to precision limitations. This can affect algorithm outcomes, particularly in minimum/maximum computations.

**Memory and Performance**: Each time a script runs, graph data must be loaded into memory. For large networks:
- Use appropriate data structures (sparse matrices for large sparse graphs)
- Consider loading only necessary subgraphs
- Use efficient file formats (pickle for Python objects, compressed formats)
- Leverage approximate algorithms for very large networks (e.g., `k` parameter in centrality calculations)
- For heavy workloads, NetworkX 3.x supports drop-in accelerated backends via the `backend=` keyword or `nx.config.backend_priority` — e.g. `nx-cugraph` (GPU), `nx-parallel` (multicore), `graphblas-algorithms` (sparse linear algebra). Install the backend package and pass `backend="cugraph"` (or similar) to supported functions; no algorithm code changes needed.

**Node and Edge Types**:
- Nodes can be any hashable Python object (numbers, strings, tuples, custom objects)
- Use meaningful identifiers for clarity
- When removing nodes, all incident edges are automatically removed

**Random Seeds**: Always set random seeds for reproducibility in random graph generation and force-directed layouts:
```python
G = nx.erdos_renyi_graph(n=100, p=0.1, seed=42)
pos = nx.spring_layout(G, seed=42)
```

## Quick Reference

### Basic Operations
```python
# Create
G = nx.Graph()
G.add_edge(1, 2)

# Query
G.number_of_nodes()
G.number_of_edges()
G.degree(1)
list(G.neighbors(1))

# Check
G.has_node(1)
G.has_edge(1, 2)
nx.is_connected(G)

# Modify
G.remove_node(1)
G.remove_edge(1, 2)
G.clear()
```

### Essential Algorithms
```python
# Paths
nx.shortest_path(G, source, target)
nx.all_pairs_shortest_path(G)

# Centrality
nx.degree_centrality(G)
nx.betweenness_centrality(G)
nx.closeness_centrality(G)
nx.pagerank(G)

# Clustering
nx.clustering(G)
nx.average_clustering(G)

# Components
nx.connected_components(G)
nx.strongly_connected_components(G)  # Directed

# Community
community.greedy_modularity_communities(G)
```

### File I/O Quick Reference
```python
# Read
nx.read_edgelist('file.txt')
nx.read_graphml('file.graphml')
nx.read_gml('file.gml')

# Write
nx.write_edgelist(G, 'file.txt')
nx.write_graphml(G, 'file.graphml')
nx.write_gml(G, 'file.gml')

# Pandas
nx.from_pandas_edgelist(df, 'source', 'target')
nx.to_pandas_edgelist(G)
```

## Resources

This skill includes comprehensive reference documentation:

### references/graph-basics.md
Detailed guide on graph types, creating and modifying graphs, adding nodes and edges, managing attributes, examining structure, and working with subgraphs.

### references/algorithms.md
Complete coverage of NetworkX algorithms including shortest paths, centrality measures, connectivity, clustering, community detection, flow algorithms, tree algorithms, matching, coloring, isomorphism, and graph traversal.

### references/generators.md
Comprehensive documentation on graph generators including classic graphs, random models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz), lattices, trees, social network models, and specialized generators.

### references/io.md
Complete guide to reading and writing graphs in various formats: edge lists, adjacency lists, GraphML, GML, JSON, CSV, Pandas DataFrames, NumPy arrays, SciPy sparse matrices, database integration, and format selection guidelines.

### references/visualization.md
Extensive documentation on visualization techniques including layout algorithms, customizing node and edge appearance, labels, interactive visualizations with Plotly and PyVis, 3D networks, bipartite layouts, and creating publication-quality figures.

## Additional Resources

- **Official Documentation**: https://networkx.org/documentation/latest/
- **Tutorial**: https://networkx.org/documentation/latest/tutorial.html
- **Gallery**: https://networkx.org/documentation/latest/auto_examples/index.html
- **GitHub**: https://github.com/networkx/networkx

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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