Network Science, Complex Networks course materials for the Asian Institute of Management's MSc. in Data Science program.

Click the "Open in Colab" button to run any notebook directly in Google Colab (no installation required):

#	Notebook	Description	Open in Colab
1	Intro to Network Analysis	Introduction to network analysis concepts
2	Network Models	Network generation models (ER, BA, WS)
3	Centrality Measures	Network vulnerability and robustness
4	Community Detection	Community detection in complex networks
5	Error and Attack Tolerance	Error and attack in complex networks
6	Vaccination Strategies	Application: Exploring vaccination strategies
7	Friendship Paradox	Phenomenon: Friendship paradox
8	Social Distancing	Application: Social distancing model

1. Click any "Open in Colab" button above
2. All dependencies are pre-installed in Colab
3. Start learning immediately!

• Python 3.10+
• Jupyter Notebook

# Clone the repository
git clone https://github.com/eflegara/Network-Science-Lectures.git
cd Network-Science-Lectures

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

• NetworkX - Network analysis and visualization
• Matplotlib - Plotting and visualization
• NumPy - Numerical computing
• Pandas - Data manipulation
• SciPy - Scientific computing
• lmfit - Curve fitting (for epidemiological models)

For community detection, NetworkX 2.7 and later include a built-in Louvain algorithm. If you encounter issues:

pip install python-louvain

This course covers fundamental concepts in network science:

Foundation (Notebooks 1-2)

• Basic network concepts and terminology
• Network models and generation algorithms

Analysis (Notebooks 3-4)

• Centrality measures and node importance
• Community structure and detection methods

Applications (Notebooks 5-8)

• Network robustness and resilience
• Epidemiological modeling on networks
• Social phenomena and paradoxes

These notebooks accompany the Network Science course under AIM's MSc in Data Science program, where Network Science is a core data science course.

├── notebooks/           # Jupyter notebooks (numbered sequence)
├── datasets/           # Data files used in examples
├── figures/           # Generated plots and diagrams
├── requirements.txt   # Python dependencies
└── README.md         # This file

Erika Fille Legara

• Website: erikalegara.site
• Institution: Asian Institute of Management

Found an issue or have suggestions? Please open an issue or submit a pull request.

MIT License - see LICENSE.md for details.

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