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Visualization Guide

Hypergraph-DB provides powerful interactive visualization capabilities to help you intuitively explore and understand hypergraph data structures.

🎨 Visualization Overview

Hypergraph Visualization

✨ Core Features

  • 🌐 Interactive Web Interface: Real-time visualization based on modern browsers
  • πŸ” Multi-level Exploration: Seamless switching from global views to local details
  • πŸ“Š Smart Layout: Adaptive graph layout algorithms
  • 🎯 Real-time Data: Direct reflection of current hypergraph state
  • πŸ“± Responsive Design: Support for desktop and mobile devices

πŸš€ Quick Start

Basic Usage

from hyperdb import HypergraphDB

# Create hypergraph
hg = HypergraphDB()

# Add data
hg.add_v(1, {"name": "Alice", "type": "Person"})
hg.add_v(2, {"name": "Bob", "type": "Person"}) 
hg.add_v(3, {"name": "Project A", "type": "Project"})

# Add hyperedges
hg.add_e((1, 2), {"relation": "friends"})
hg.add_e((1, 2, 3), {"relation": "collaboration"})

# Launch visualization
hg.draw()  # Automatically opens browser

Visualization Configuration

# Custom port
hg.draw(port=8888)

# Generate HTML without opening browser
viewer = hg.get_viewer(auto_open=False)
html_content = viewer.get_html()

πŸ“š Detailed Feature Guide

πŸ“– Section πŸ“‹ Content
Basic Operations Navigation, zooming, selection and other basic operations
Interface Guide Detailed description of user interface components
Advanced Customization Advanced customization options and techniques

🎯 Use Cases

πŸ“Š Data Exploration

  • Relationship Analysis: Understanding complex multi-way relationships
  • Pattern Discovery: Identifying important patterns in data
  • Anomaly Detection: Finding outliers in data

πŸ“ˆ Research Applications

  • Social Networks: Analyzing multi-person group interactions
  • Knowledge Graphs: Visualizing complex relationships between concepts
  • Biological Networks: Displaying protein interaction networks

πŸŽ“ Teaching and Demonstration

  • Algorithm Visualization: Showing graph algorithm execution
  • Concept Explanation: Intuitive explanation of hypergraph theory
  • Case Studies: Demonstration of real-world applications

πŸ”§ Technical Architecture

Frontend Technology Stack

  • React 18: Modern user interface
  • G6 Graph Library: Professional graph visualization engine
  • Tailwind CSS: Elegant styling system

Backend Integration

  • Python HTTP Server: Lightweight local server
  • JSON Data Transfer: Efficient data exchange format
  • Real-time Sync: Instant reflection of data changes

πŸ“± Browser Compatibility

🌐 Browser βœ… Supported Version πŸ“‹ Notes
Chrome 90+ Recommended, best performance
Firefox 88+ Fully supported
Safari 14+ Fully supported
Edge 90+ Fully supported

🚨 Important Notes

Performance Considerations

  • Large-scale Data: For more than 1000 vertices, filtering is recommended
  • Memory Usage: Visualization consumes browser memory
  • Network Ports: Ensure specified port is not occupied

Security Reminders

  • Local Server: For local development only, not recommended for public exposure
  • Data Sensitivity: Visualization displays all data in browser

Ready to start exploring the visual world of hypergraphs? Begin your visualization journey with the Basic Operations Guide!