on GitHub
Educational materials for the textbook 'A First Course in Network Science', including Python tutorials, datasets, and Jupyter notebooks.
What is on GitHub?
A First Course in Network Science is a collection of educational resources accompanying the Cambridge University Press textbook of the same name. It provides tutorials, datasets, and teaching materials to help students learn network science through hands-on programming exercises. The materials cover fundamental concepts like small-world networks, hubs, communities, and network dynamics using real-world examples.
Undergraduate and graduate students from diverse fields including informatics, business, computer science, biology, physics, and social sciences who need to understand network analysis. Also valuable for instructors teaching network science courses.
It offers a practical, accessible introduction to network science without requiring advanced mathematics or programming background. The hands-on tutorials with real datasets and multiple deployment options (cloud/local) lower the barrier to entry while maintaining academic rigor.
Overview
Tutorials, datasets, and other material associated with textbook "A First Course in Network Science" by Menczer, Fortunato & Davis
Use Cases
Best For
- Teaching introductory network science courses at university level
- Learning network analysis through hands-on Python programming
- Accessing curated network datasets for research projects
- Understanding social network analysis and community detection
- Exploring network models and dynamics with practical examples
- Supplementing textbook learning with interactive Jupyter notebooks
Not Ideal For
- Researchers seeking advanced or cutting-edge network science algorithms beyond introductory material
- Industry professionals needing production-ready network analysis tools or APIs for deployment
- Self-learners without access to the Cambridge University Press textbook, as some resources require instructor registration
- Projects requiring real-time or large-scale network analysis with performance-optimized code
Pros & Cons
Pros
Tutorials use curated real-world datasets from the repository, allowing students to apply network science concepts practically without relying on synthetic examples.
The materials require no prior mathematical or programming expertise, with optional technical sections for advanced students, making it inclusive for beginners across disciplines.
Supports cloud services like Google Colab and Binder, or local setup via Anaconda, as noted in the README, catering to different technical environments and user preferences.
Includes Jupyter notebook tutorials, datasets, sample slides, and exercise solutions, providing a full suite of resources for both students and instructors.
Cons
Exercise solutions and all lecture slides are locked behind instructor registration on the Cambridge University Press website, limiting accessibility for independent learners.
The README explicitly states that local Python installations can be problematic, especially on Windows, and no support is provided, which may frustrate users preferring offline work.
Based on the 2020 textbook edition, the tutorials may not include recent advancements in network science or updates to dependencies like Python libraries, risking compatibility issues.
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