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<span>Book Core + Course Hub</span>
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**This site is under construction.** It will become the home of the *Computational Methods for Network Science* textbook. Chapters are being written and revised through the current semester, and the first set will be published here once the Fall 2026 course wraps. In the meantime, this page is the hub for the courses built on the material — each links out to its own course book below.
Computational Methods for Network Science is a modular, notebook-first textbook for learning how to work with real network data, simulations, and models, from first principles through advanced topics. It is designed as a shared core that can be taught as one long sequence, remixed into course-specific books, or used as a reference for research.
Most network science resources are either theory-forward but short on reproducible code, or code-heavy tutorials that never connect the methods into a coherent curriculum. This project is built to close that gap, combining mathematically grounded explanations, transparent and runnable Python notebooks, and direct connections between methods, data, and research questions.
- data ingestion, cleaning, and network construction from raw tables and web data
- core graph statistics, path algorithms, centralities, and distributional analysis
- community detection, spectral methods, and mesoscale structure
- dynamics on and of networks: diffusion, epidemics, agent-based models, and temporal systems
- sampling, sparsification, reconstruction, and inference strategies
- machine learning on graphs, embeddings, and advanced complex-systems topics
- students taking an undergraduate or graduate course in networks
- instructors assembling a syllabus from reusable chapter modules
- researchers who need practical examples for network pipelines
- practitioners moving from toy examples to real data
Each course below is taught from this material and has its own standalone book.
NETS/PHYS 7052 — Computational Methods for Network Science · Fall 2026 · Northeastern University Course book · Repository
PHYS 7332 — Network Science Data II · Fall 2025 Course book · Repository
PHYS 7332 — Network Science Data II · Fall 2024 Course book
CNET 5052 — Advanced Tools for Complex Network Analysis · Spring 2026 Repository
Brennan Klein is an Assistant Professor in the Department of Communication Studies and the Department of Physics at Northeastern University, and core faculty at the Network Science Institute, where he directs the Complexity & Society Lab. His work spans methods for understanding information, emergence, and communication in complex systems; data justice and large-scale public-interest data science; and practical, reproducible computational workflows for network analysis. Website: brennanklein.com.
This project reflects several years of course development, student projects, and research collaboration. It builds in particular on course and research development by Matteo Chinazzi, Qian Zhang, Alyssa Smith, and Milo Trujillo.
Many chapters grew out of final project work by students in earlier iterations of these courses. Detailed, chapter-level attributions will be published alongside each chapter.