PHYS 7332: Network Science Data & Models – Fall 2025

PHYS 7332: Network Science Data & Models – Fall 2025#

Welcome to PHYS 7332! This repository hosts each week’s Jupyter notebooks and supporting materials.

Course Overview#

This course offers an introduction to network analysis and is designed to provide students with an overview of the core data scientific skills required to analyze complex networks. Through hands-on lectures, labs, and projects, students will learn actionable skills about network analysis techniques using Python (in particular, the networkx library). The course network data collection, data input/output, network statistics, dynamics, and visualization. Students also learn about random graph models and algorithms for computing network properties like path lengths, clustering, degree distributions, and community structure. In addition, students will develop web scraping skills and will be introduced to the vast landscape of software tools for analyzing complex networks. The course ends with a large-scale final project that demonstrates the proficiency of the students in network analysis. This course has been built from the foundation of the years of work/development by Matteo Chinazzi and Qian Zhang, for earlier iterations of Network Science Data.

Course Website: https://network-science-data-and-models.github.io/phys7332_fa25/

Github Repository: https://github.com/network-science-data-and-models/phys7332_fa25/

Instructors#

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/.

Course Learning Outcomes#

Students should leave this class with an ever-growing codebase of resources for analyzing and deriving insights from complex networks, using Python. These skills range from being able to (from scratch) code algorithms on graphs, including path length calculations, network sampling, dynamical processes, and network null models; as well as interfacing with standard data science questions around storing, querying, and analyzing large complex datasets.

  • Proficiency in Python and networkx for network analysis.

  • Strong foundation of complex network algorithms and their applications.

  • Skills in statistical description of networks.

  • Experience in collecting and analyzing online data.

  • Broad knowledge of various network libraries and tools.

Materials#

There are no required materials for this course, but we will periodically draw from:

Additionally, we recommend engagement with other useful network science and/or Python materials:

Coursework, Class Structure, Grading#

This is a twice-weekly hands-on class that emphasizes building experience with coding. This does not necessarily mean every second of every class will be live-coding, but it will inevitably come up in how the class is taught. We are often on the lookout for improving the pedagogical approach to this material, and we would welcome feedback on class structure. The course will be co-taught, featuring lectures from the core instructors as well as outside experts. Grading in this course will be as follows:

  • Class Attendance & Participation: 10%

  • Problem Sets: 45%

  • Mid-Semester Project Presentation: 15%

  • Final Project — Presentation & Report: 30%

Final Project#

The final project for this course is a chance for students to synthesize their knowledge of network analysis into pedagogical materials around a topic of their choosing. Modeled after chapters in the Jupyter book for this course, students will be required to make a new “chapter” for our class’s textbook; this requires creating a thoroughly documented, informative Python notebook that explains an advanced topic that was not deeply explored in the course. For these projects, students are required to conduct their own research into the background of the technique, the original paper(s) introducing the topic, and how/if it is currently used in today’s network analysis literature. Students will demonstrate that they have mastered this technique by using informative data for illustrating the usefulness of the topic they’ve chosen. Every chapter should contain informative data visualizations that build on one another, section-by-section. The purpose of this assignment is to demonstrate the coding skills gained in this course, doing so by learning a new network analysis technique and sharing it with members of the class. Over time, these lessons may find their way into the curriculum for future iterations of this class. Halfway through the semester, there will be project update presentations where students receive class and instructor feedback on their project topics. Throughout, we will be available to brainstorm students’ ideas for project topics.

Ideas for Final Project Chapters (non-exhaustive)#

  1. Network Geometry and Random Hyperbolic Graphs

  2. Information Theory in/of Complex Networks

  3. Simple vs. Complex Contagion

  4. Self-organized Criticality in Networks

  5. Hypergraphs

  6. Multilayer Urban Networks

  7. Temporal Community Detection

  8. Motifs in Networks

  9. Signed Networks

  10. Percolation in Networks

  11. Coarse Graining Networks

  12. Graph Curvature or Topology

  13. Adaptive Networks

  14. Network Game Theory (Prisoner’s Dilemma, Schelling Model, etc.)

  15. Network Rewiring Dynamics

  16. Fitting Distributions to Network Data

  17. Hierarchical Networks

  18. Ranking in Networks

  19. Mechanistic vs Statistical Network Models

  20. Discrete Models of Network Dynamics (Voter model, Ising model, SIS, etc.)

  21. Continuous Models of Network Dynamics (Kuramoto model, Lotka-Volterra model, etc.)

  22. Robustness / Resilience of Network Structure

  23. Mesoscale Structure in Networks (e.g. core-periphery)

  24. Graph Isomorphism and Approximate Isomorphism

  25. Inference in Networks: Beyond Community Detection

  26. Activity-Driven Network Models

  27. Forecasting with Networks

  28. Higher-Order Networks

  29. Introduction to Graph Neural Networks

  30. Hopfield Networks and Boltzmann Machines

  31. Reservoir Computing

  32. Multiplex/Multilayer Networks

  33. Graph Summarization Techniques

  34. Network Anomalies

  35. Modeling Cascading Failures

  36. Topological Data Analysis in Networks

  37. Deeper Dive: Random Walks on Networks

  38. Deeper Dive: Directed Networks

  39. Deeper Dive: Network Communities

  40. Deeper Dive: Network Null Models

  41. Deeper Dive: Network Paths and their Statistics

  42. Deeper Dive: Network Growth Models

  43. Deeper Dive: Network Sampling

  44. Deeper Dive: Spatially-Embedded and Urban Networks

  45. Deeper Dive: Hypothesis Testing in Social Networks

  46. Deeper Dive: Working with Massive Data

  47. Deeper Dive: Bipartite Networks

  48. Many more possible ideas! Send us whatever you come up with

Schedule#

DATE

CLASS

Mon, Sep 1, 25

Labor Day

Wed, Sep 3, 25

Class 0: Introduction to the Course, GitHub, Computing Setup

Fri, Sep 5, 25

Class 1: Python Refresher (Data Structures, NumPy)

Mon, Sep 8, 25

—

Wed, Sep 10, 25

Class 2: Introduction to NetworkX 1 — Loading Data, Basic Statistics

Fri, Sep 12, 25

Class 3: Introduction to NetworkX 2 — Graph Algorithms

Mon, Sep 15, 25

Announce Assignment 1

Wed, Sep 17, 25

Class 4: Distributions of Network Properties & Centralities

Fri, Sep 19, 25

Class 5: Scraping Web Data 1 — BeautifulSoup, HTML, Pandas

Mon, Sep 22, 25

—

Wed, Sep 24, 25

Class 6: Data Science & SQL

Fri, Sep 26, 25

Class 7: Scraping Web Data 2 — Creating a Network from SQL Data

Mon, Sep 29, 25

Assignment 1 due September 29

Wed, Oct 1, 25

Class 8: Clustering & Community Detection 1 — Traditional

Fri, Oct 3, 25

Class 9: Clustering & Community Detection 2 — Contemporary

Mon, Oct 6, 25

Announce Assignment 2

Wed, Oct 8, 25

~~Class 10: Clustering & Community Detection 3 — Advanced ~~

Fri, Oct 10, 25

Class 11: Project Update Presentations

Mon, Oct 13, 25

Indigenous Peoples’ Day

Wed, Oct 15, 25

Class 12: Visualization 1 — Python

Fri, Oct 17, 25

Class 13: Visualization 2 — Advanced Python

Mon, Oct 20, 25

Assignment 2 due October 20

Wed, Oct 22, 25

Class 14: Introduction to Machine Learning 1 — General

Fri, Oct 24, 25

Class 15: Introduction to Machine Learning 2 — Networks

Mon, Oct 27, 25

—

Wed, Oct 29, 25

Class 16: Dynamics on Networks 1 — Diffusion and Random Walks

Fri, Oct 31, 25

Class 17: Dynamics on Networks 2 — Compartmental Models

Mon, Nov 3, 25

Announce Assignment 3

Wed, Nov 5, 25

Class 18: Network Comparison & Graph Distances (Chp 19)

Fri, Nov 7, 25

Class 19: Dynamics on Networks 3 — Agent‑Based Models (Chp 18)

Mon, Nov 10, 25

—

Wed, Nov 12, 25

Class 20: Dynamics of Networks — Temporal Networks

Fri, Nov 14, 25

Class 21: Network Filtering / Thresholding

Mon, Nov 17, 25

—

Wed, Nov 19, 25

Class 22: Network Sampling: Theory & Applications

Fri, Nov 21, 25

Class 23: Network Reconstruction from Dynamics

Mon, Nov 24, 25

Assignment 3 due November 24

Wed, Nov 26, 25

Thanksgiving Break (No Class)

Fri, Nov 28, 25

—

Mon, Dec 1, 25

—

Wed, Dec 3, 25

Class 24: Spatial Data, OSMnx, GeoPandas

Fri, Dec 5, 25

Class 25: Final Presentations

Mon, Dec 8, 25

—

Wed, Dec 10, 25

No class

Fri, Dec 12, 25

No class

Repository Contents#

  • notebooks/class_##/: Weekly lesson notebooks and in-class demos.

  • requirements.txt: Python dependencies; install via pip install -r requirements.txt.

  • _config.yml and _toc.yml: Jupyter Book configuration and table of contents.

Getting Started#

  1. Clone the repo

    git clone https://github.com/network-science-data-and-models/phys7332_fa25.git
cd phys7332_fa25
Contents