FollowWeb Network Analysis Package

A network analysis tool for Instagram follower/following relationships using graph theory and network analysis techniques. Transform social connection data into interactive visualizations with automatic community detection and influence metrics.

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Key Features

• Multiple Analysis Strategies: k-core decomposition, reciprocal connections, ego-alter analysis
• Comprehensive Reporting: text reports with network statistics and parameters
• Performance Optimized: caching system eliminates duplicate calculations and reduces memory usage

Analysis Strategies

1. K-Core Analysis: Full network analysis identifying densely connected subgraphs
2. Reciprocal K-Core: Focus on mutual connections and bidirectional relationships
3. Ego-Alter Analysis: Personal network analysis centered on specific users

Output Formats

• Interactive HTML: Network visualizations with hover tooltips and physics controls
• Static PNG: High-resolution images suitable for presentations and papers
• Metrics Reports: Detailed analysis statistics, timing, and configuration parameters

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Quick Setup

Installation

# Install production dependencies
pip install -r requirements.txt

# Install the package in development mode
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"

Basic Usage

# Run analysis with sample data
followweb --input examples/followers_following.json

# Use a configuration file
followweb --config configs/fast_config.json

# Print default configuration
followweb --print-default-config

Example Configuration Files

• fast_config.json - Quick analysis optimized for development and testing
• comprehensive_layout_config.json - Complete configuration with all available features

Development Setup

For development, see docs/development/CONTRIBUTING.md for detailed setup instructions including dependency installation and code quality tools.

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Testing

FollowWeb includes a comprehensive test suite with 337 passing tests and 73.95% code coverage, ensuring reliability across all components.

Test Categories

• Unit Tests (280+ tests): Fast, isolated component testing with maximum parallelization
• Integration Tests (45+ tests): Cross-module testing with controlled parallelization
• Performance Tests (12+ tests): Benchmarking and timing validation with sequential execution

Running Tests

# Run all tests with coverage
python -m pytest --cov=FollowWeb_Visualizor --cov-report=term-missing

# Run specific test categories
python -m pytest tests/unit/          # Unit tests only
python -m pytest tests/integration/   # Integration tests only
python -m pytest tests/performance/   # Performance tests only

# Run tests with detailed output
python -m pytest -v

# Run tests in parallel (automatic)
python -m pytest -n auto

For detailed testing procedures, see tests/README.md.

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Documentation

User Documentation

• docs/USER_GUIDE.md - User guide with tutorials and workflows
• docs/CONFIGURATION_GUIDE.md - Configuration guide with layout options
• docs/INSTALL_GUIDE.md - Installation and setup guide
• tests/README.md - Testing procedures and guidelines

Developer Documentation

• docs/development/CONTRIBUTING.md - Development guidelines and contribution process
• docs/development/API_REFERENCE.md - Complete API documentation with examples
• docs/development/PACKAGE_OVERVIEW.md - High-level package architecture overview
• docs/development/PARALLEL_PROCESSING_GUIDE.md - Centralized parallel processing system guide

Package Structure

├── FollowWeb_Visualizor/    # Main package (main.py, config.py, analysis.py, visualization.py, utils.py, progress.py)
├── tests/                   # Test suite (unit/, integration/, performance/)
├── docs/                    # Documentation (API_REFERENCE.md, USER_GUIDE.md, CONTRIBUTING.md, etc.)
│   └── development/         # Development documentation and analysis reports
├── configs/                 # Configuration files for different analysis scenarios
├── examples/                # Sample data and example outputs
├── Output/                  # Default output directory for generated results
├── README.md               # Main documentation
├── setup.py                # Package installation
├── requirements*.txt       # Dependencies
├── Makefile               # Development automation
└── pytest.ini            # Test configuration

Acknowledgments

FollowWeb is built upon excellent open-source libraries and tools. We gratefully acknowledge:

Core Dependencies

• NetworkX - Graph analysis algorithms and community detection
• pandas - Data manipulation and analysis
• matplotlib - Static graph visualization and plotting
• pyvis - Interactive network visualizations

Development Tools

• pytest ecosystem - Comprehensive testing framework
• ruff, mypy - Code quality tools

See docs/attribution/CONTRIBUTORS.md for detailed acknowledgments and contribution guidelines.

Links

• Source Code: FollowWeb_Visualizor/
• Tests: tests/
• Documentation: docs/
• Attribution: docs/attribution/ - Contributors, dependencies, and license notices