netx-vis 0.2.2

Convert NetworkX graphs to interactive HTML visualizations

NetworkX HTML Viewer 🎯

Convert your NetworkX graphs into beautiful, interactive HTML visualizations with just one line of code!

✨ Click any node or edge to see all its properties in a sidebar - perfect for exploring complex networks with rich metadata.

🚀 Features

• 🎯 Interactive Property Viewing: Click nodes/edges to see all their attributes
• 🎨 Beautiful UI: Modern, professional interface with sidebar property panel
• 🔍 Zoom & Pan: Explore large graphs with smooth zoom and pan controls
• 🎮 Drag Nodes: Rearrange graph layout by dragging nodes
• 🏷️ Toggle Labels: Show/hide node labels as needed
• 📊 Graph Statistics: Display node count, edge count, and graph type
• 🎨 Force-Directed Layout: Automatic layout using D3.js physics simulation
• 💾 Export Ready: Generates standalone HTML files
• 🔧 Highly Customizable: Adjust dimensions, styling, and behavior

📦 Installation

pip install netx-vis

🎮 Quick Start

import networkx as nx
from networkx_html_viewer import convert_networkx_to_html

# Create a sample graph with properties
G = nx.Graph()
G.add_node("A", label="Node A", type="central", value=100, category="important")
G.add_node("B", label="Node B", type="peripheral", value=50, category="normal") 
G.add_edge("A", "B", weight=0.8, relation="strong", type="primary")

# Convert to interactive HTML - that's it! 🎉
html_file = convert_networkx_to_html(
    graph=G,
    output_file="my_network.html", 
    title="My Amazing Network",
    width=1200,
    height=800
)

print(f"Interactive graph saved to: {html_file}")
# Open the HTML file in your browser and start exploring!

🎯 Interactive Features

Once you open the HTML file in your browser:

1. 🔵 Click any node (colored circles) to see all its properties
2. 🔗 Click any edge (lines) to see connection details
3. 🖱️ Drag nodes to rearrange the layout
4. 🔍 Zoom with mouse wheel or trackpad
5. 🤏 Pan by dragging empty space
6. 🏷️ Toggle labels with the button
7. 🔄 Reset view to recenter the graph
8. 🔥 Reheat simulation to restart the physics

🔧 Advanced Usage

Using the Class Interface

from networkx_html_viewer import NetworkXHTMLConverter

# Create converter with custom dimensions
converter = NetworkXHTMLConverter(width=1600, height=1000)

# Convert graph
html_file = converter.convert(
    G, 
    output_file="advanced_graph.html",
    title="Advanced Network Analysis"
)

# Or get HTML content as string (for embedding in web apps)
html_content = converter.preview(G, title="Preview Graph")

Working with Complex Graphs

import networkx as nx

# Create a more complex graph
G = nx.karate_club_graph()

# Add custom properties to nodes
for node in G.nodes():
    G.nodes[node]['club'] = G.nodes[node].get('club', 'unknown')
    G.nodes[node]['degree'] = G.degree(node)
    G.nodes[node]['betweenness'] = nx.betweenness_centrality(G)[node]

# Add properties to edges
for edge in G.edges():
    G.edges[edge]['weight'] = G.edges[edge].get('weight', 1.0)
    G.edges[edge]['edge_betweenness'] = nx.edge_betweenness_centrality(G)[edge]

# Add graph-level properties
G.graph['name'] = 'Karate Club Network'
G.graph['description'] = 'Social network of a university karate club'
G.graph['nodes'] = G.number_of_nodes()
G.graph['edges'] = G.number_of_edges()

# Convert to HTML
convert_networkx_to_html(G, "karate_club.html", "Karate Club Social Network")

Supported Graph Types

All NetworkX graph types are supported:

• nx.Graph() - Undirected graphs
• nx.DiGraph() - Directed graphs
• nx.MultiGraph() - Undirected multigraphs
• nx.MultiDiGraph() - Directed multigraphs

🎨 Customization Options

# Customize dimensions and output
converter = NetworkXHTMLConverter(
    width=1920,    # Canvas width in pixels
    height=1080    # Canvas height in pixels
)

# The HTML template automatically adapts to your graph:
# - Node sizes scale with number of properties
# - Edge thickness reflects property count
# - Colors are automatically assigned
# - Layout adjusts to graph structure

📊 What Properties Are Displayed?

The viewer shows ALL properties from your NetworkX graph:

• Node Properties: Any attributes you add to nodes
• Edge Properties: Any attributes you add to edges
• Graph Properties: Graph-level metadata
• Computed Properties: Centrality measures, clustering coefficients, etc.

Example of rich property data:

# Nodes can have any properties
G.add_node("user123", 
    name="Alice Johnson",
    age=29,
    location="San Francisco", 
    followers=1250,
    verified=True,
    interests=["AI", "Python", "Networks"]
)

# Edges can have any properties  
G.add_edge("user123", "user456",
    relationship="friend",
    strength=0.8,
    since="2019-03-15",
    interactions=247,
    platforms=["twitter", "linkedin"]
)

# All of this will be displayed when you click!

🛠️ Development Setup

# Clone repository
git clone https://github.com/olsihoxha/netx-vis.git
cd netx-vis

# Install in development mode
pip install -e .

# Install development dependencies
pip install pytest black flake8 mypy

# Run tests
pytest tests/

# Format code
black networkx_html_viewer/

📁 Package Structure

netx-vis/
├── networkx_html_viewer/
│   ├── __init__.py              # Package initialization
│   ├── converter.py             # Main converter class
│   ├── templates/
│      └── graph_template.html  # HTML template with D3.js
├── tests/
│   └── test_converter.py        # Unit tests
├── setup.py                     # Package configuration
├── requirements.txt             # Dependencies
├── README.md                    # This file
├── LICENSE                      # MIT License
└── MANIFEST.in                  # Package data files

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with D3.js for interactive visualizations
• Inspired by the NetworkX community
• Thanks to all contributors and users!

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Made with ❤️ for the NetworkX community

Convert your graphs. Explore your data. Share your insights. 🎯