Network Science Data & Models

Network Science Data & Models#

Welcome to the official textbook for Network Science Data & Models. This site is meant to serve as:

  • A living, open-access textbook
    We cover the core concepts, algorithms, and datasets you’ll need to explore networks in the wild.

  • A reference for researchers & practitioners
    Detailed examples, code snippets, and real-world datasets illustrate how to load, analyze, and visualize networks at scale.

  • A companion to course offerings
    While this book stands on its own, it also underpins various semester-long courses (e.g., PHYS 7332 at Northeastern University). If you’re here to follow along with a class, see the separate course repo for lecture-by-lecture guidance and assignments.

What’s Inside#

  • Chapters
    Each chapter dives into a major topic in network science: graph representations, centrality & community detection, dynamics on networks, big-data scalability, spatial networks, and more.

  • Code & Data
    Executable Jupyter notebooks live alongside each chapter—feel free to clone the repo, run them locally, and experiment with the supplied datasets.

  • Bibliography
    All references are managed in references.bib. Citations appear inline, so you can trace back to the original literature.

Contributors#

Brennan Klein is core faculty at the Network Science Institute at Northeastern University and Assistant Teaching Professor in the Department of Physics. He is the director of the Complexity & Society Lab, which spans two broad research areas: 1) Information, emergence, and inference in complex systems—developing tools and theory for characterizing dynamics, structure, and scale in networks, and 2) Public health and public safety—creating and analyzing large-scale datasets that reveal inequalities in the U.S., from epidemics to mass incarceration. In 2023, Prof. Klein was awarded the René Thom Young Researcher Award, given to a researcher to recognize substantial early career contributions and leadership in research in Complex Systems-related fields. He received a PhD in Network Science in 2020 from Northeastern University and a BA in Cognitive Science from Swarthmore College in 2014. Website: http://brennanklein.com/.

Alyssa Smith is a PhD student in Network Science at Northeastern University. Her current work focuses on the ways that structure and agency interact in social networks to encourage mobilization. She is interested in making big data and computational tools usable by academics without specialized technical training. She use mixed methods, ranging from terabyte-scale datasets to autoethnography, to make sense of the world. Her dissertation work revolves around structure – the place one occupies in a social network – and agency – an individual’s characteristics and proclivities – which are thought to be the two main driving forces behind engagement in social movements. We can think of structure and agency as two separate, competing factors, or we can think of them as a duality: in much the same way that light is both a particle and a wave, the interplay of structure and agency is what governs mobilization. Before joining the Network Science Institute, Alyssa received a BS in Humanities and Engineering with Comparative Media Studies and Computer Science from MIT in 2017; after that, she worked in tech for 4 years. Website: https://asmithh.github.io/.

Matteo Chinazzi is a Research Associate Professor at Northeastern University, a member of the Roux Institute, and core faculty at the Network Science Institute. His work spans network science, data science, epidemiology, economics, and artificial intelligence, with a focus on developing large-scale, data-driven computational and agent-based models to study and forecast the spatial spread of infectious diseases—incorporating human behavior changes (mobility, contact patterns, vaccine hesitancy) and policy interventions—and on designing hybrid frameworks that couple mechanistic epidemic models with machine learning and deep learning approaches. Prof. Chinazzi also investigates human mobility using high-resolution location data and studies the evolution and structure of science and innovation. Website: matteochinazzi.com.

Qian Zhang is a multidisciplinary researcher and Principal Machine Learning Engineer at Liftoff Mobile, where she leads the development of feed-ranking and content-recommendation systems. Prior to Liftoff, she served as Principal Machine Learning Engineer at Contagious Health, and at S&P Global, and was a Senior Data Scientist at Twitter and an Applied Scientist at Amazon. She holds a PhD in Computer Science from Northeastern University. Dr. Zhang’s research has spanned data mining, analysis, statistical modeling of large-scale datasets, and the simulation of infectious-disease spreading on dynamic networks. Website: zhangqianrach.org.

Contributing#

We welcome pull requests! If you find typos, want to extend a chapter, or add a new case study, please fork the repo, make your changes, and open a PR against main.

Enjoy exploring the data and models that power modern network science!

Contents