Welcome to PHYS 7332! This repository hosts each week's Jupyter notebooks and supporting materials.
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/
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/.
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
networkxfor 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.
There are no required materials for this course, but we will periodically draw from:
- Bagrow & Ahn (2024). Working with Network Data: A Data Science Perspective. Cambridge University Press; 1st Edition; 978-1009212595. https://www.cambridge.org/network-data
Additionally, we recommend engagement with other useful network science and/or Python materials:
- Barabási (2016). Network Science. Cambridge University Press; 1st Edition; 978-1107076266. http://networksciencebook.com/
- Newman (2018). Networks: An Introduction. Oxford University Press; 2nd Edition; 978-0198805090. https://global.oup.com/academic/product/networks-9780198805090
- Barrat, Barthelemy, & Vespignani (2008). Dynamical Processes on Complex Networks. Cambridge University Press; 1st Edition; 978-0511791383. https://doi.org/10.1017/CBO9780511791383
- VanderPlas (2019). Python Data Science Handbook. O'Reilly Media, Inc; 978-1491912058. https://github.com/jakevdp/PythonDataScienceHandbook
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%
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.
- Motifs in Networks
- Mechanistic vs Statistical Network Models
- Robustness / Resilience of Network Structure
- Network Game Theory (Prisoner’s Dilemma, Schelling Model, etc.)
- Homophily in Networks
- Network Geometry and Random Hyperbolic Graphs
- Information Theory in/of Complex Networks
- Discrete Models of Network Dynamics (Voter model, Ising model, SIS, etc.)
- Continuous Models of Network Dynamics (Kuramoto model, Lotka-Volterra model, etc.)
- Percolation in Networks
- Signed Networks
- Coarse Graining Networks
- Mesoscale Structure in Networks (e.g. core-periphery)
- Graph Isomorphism and Approximate Isomorphism
- Inference in Networks: Beyond Community Detection
- Activity-Driven Network Models
- Forecasting with Networks
- Higher-Order Networks
- Introduction to Graph Neural Networks
- Hopfield Networks and Boltzmann Machines
- Graph Curvature or Topology
- Reservoir Computing
- Adaptive Networks
- Multiplex/Multilayer Networks
- Simple vs. Complex Contagion
- Graph Summarization Techniques
- Network Anomalies
- Modeling Cascading Failures
- Topological Data Analysis in Networks
- Self-organized Criticality in Networks
- Network Rewiring Dynamics
- Fitting Distributions to Network Data
- Hierarchical Networks
- Ranking in Networks
- Deeper Dive: Random Walks on Networks
- Deeper Dive: Directed Networks
- Deeper Dive: Network Communities
- Deeper Dive: Network Null Models
- Deeper Dive: Network Paths and their Statistics
- Deeper Dive: Network Growth Models
- Deeper Dive: Network Sampling
- Deeper Dive: Spatially-Embedded and Urban Networks
- Deeper Dive: Hypothesis Testing in Social Networks
- Deeper Dive: Working with Massive Data
- Deeper Dive: Bipartite Networks
- Many more possible ideas! Send us whatever you come up with
| 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 — Guest Lecture (Pedro Cruz, Northeastern) |
| 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 | Announce Assignment 3 |
| 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 | --- |
| Wed, Nov 5, 25 | Class 18: Dynamics on Networks 3 — Agent‑Based Models |
| Fri, Nov 7, 25 | Class 19: Network Sampling |
| Mon, Nov 10, 25 | Assignment 3 due November 10 |
| Wed, Nov 12, 25 | Class 20: Network Filtering / Thresholding |
| Fri, Nov 14, 25 | Class 21: Dynamics of Networks — Temporal Networks |
| Mon, Nov 17, 25 | --- |
| Wed, Nov 19, 25 | Class 22: Network Comparison & Graph Distances |
| Fri, Nov 21, 25 | Class 23: Network Reconstruction from Dynamics |
| Mon, Nov 24, 25 | Thanksgiving Break (No Class) |
| Wed, Nov 26, 25 | --- |
| Fri, Nov 28, 25 | --- |
| Mon, Dec 1, 25 | --- |
| Wed, Dec 3, 25 | Class 24: Big Data — Scalability & Cluster Computing |
| Fri, Dec 5, 25 | Class 25: Spatial Data, OSMnx, GeoPandas |
| Mon, Dec 8, 25 | --- |
| Thu, Dec 11, 25 | Class 26: Final Presentations |
| Fri, Dec 12, 25 | --- |
- 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.
- Clone the repo
git clone https://github.com/network-science-data-and-models/phys7332_fa25.git cd phys7332_fa25