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. This syllabus may be updated and can be found here: https://brennanklein.com/phys7332-fall24.
Our course is a Jupyter Book! Find it here: https://asmithh.github.io/network-science-data-book/.
- 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.
- Graph Embedding (or other ML technique)
- Network Reconstruction from Dynamics
- Link Prediction
- Graph Distances and Network Comparison
- Motifs in Networks
- Network Sparsification
- Spectral Properties of 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
Brennan Klein is an associate research scientist at the Network Science Institute, with a joint affiliation at the Institute for Experiential AI. He is the director of the Complexity & Society Lab. His research spans two broad topics: 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 United States, from epidemics to mass incarceration. Dr.Klein received a PhD in Network Science in 2020 from Northeastern University and got his BA in Cognitive Science & Psychology from Swarthmore College in 2014. Website: http://brennanklein.com/.
Alyssa Smith is a fourth-year 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/.
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.
| DATE | CLASS |
|---|---|
| Wed, Sep 4, 24 | Class 0: Introduction to the Course, Github, Computing Setup |
| Thu, Sep 5, 24 | Class 1: Python Refresher (Data Structures, Numpy) |
| Fri, Sep 6, 24 | --- |
| Wed, Sep 11, 24 | Class 2: Introduction to Networkx 1 — Loading Data, Basic Statistics |
| Thu, Sep 12, 24 | Class 3: Introduction to Networkx 2 — Graph Algorithms |
| Fri, Sep 13, 24 | Announce Assignment 1 |
| Wed, Sep 18, 24 | Class 4: Distributions of Network Properties & Centralities |
| Thu, Sep 19, 24 | Class 5: Scraping Web Data 1 — BeautifulSoup, HTML, Pandas |
| Fri, Sep 20, 24 | --- |
| Wed, Sep 25, 24 | Class 6: Scraping Web Data 2 — Creating a Network from Scraped Data |
| Thu, Sep 26, 24 | Class 7: Big Data 1 — Algorithmic Complexity & Computing Paths |
| Fri, Sep 27, 24 | Assignment 1 due September 27 |
| Wed, Oct 2, 24 | Class 8: Data Science 1 — Pandas, SQL, Regressions |
| Thu, Oct 3, 24 | Class 9: Data Science 2 — Querying SQL Tables for Network Construction |
| Fri, Oct 4, 24 | Announce Assignment 2 |
| Wed, Oct 9, 24 | Class 10: Clustering & Community Detection 1 — Traditional |
| Thu, Oct 10, 24 | Class 11: Clustering & Community Detection 2 — Contemporary |
| Fri, Oct 11, 24 | --- |
| Wed, Oct 16, 24 | Class 12: Visualization 1 — Python + Gephi |
| Thu, Oct 17, 24 | Class 13: Project Update Presentations |
| Fri, Oct 18, 24 | Assignment 2 due October 18 |
| Wed, Oct 23, 24 | Class 14: Introduction to Machine Learning 1 — General |
| Thu, Oct 24, 24 | Class 15: Introduction to Machine Learning 2 — Networks |
| Fri, Oct 25, 24 | Announce Assignment 3 |
| Wed, Oct 30, 24 | Class 16: Visualization 2 — Guest Lecture (Pedro Cruz, Northeastern) |
| Thu, Oct 31, 24 | Class 17: Dynamics on Networks 1 — Diffusion and Random Walks |
| Fri, Nov 1, 24 | --- |
| Wed, Nov 6, 24 | Class 18: Dynamics on Networks 2 — Compartmental Models |
| Thu, Nov 7, 24 | Class 19: Dynamics on Networks 3 — Agent-Based Models |
| Fri, Nov 8, 24 | Assignment 3 due November 8 |
| Wed, Nov 13, 24 | Class 20: Big Data 2 — Scalability |
| Thu, Nov 14, 24 | Class 21: Network Sampling |
| Fri, Nov 15, 24 | --- |
| Wed, Nov 20, 24 | Class 22: Network Filtering/Thresholding |
| Thu, Nov 21, 24 | Class 23: Dynamic of Networks: Temporal Networks |
| Fri, Nov 22, 24 | --- |
| Wed, Nov 27, 24 | Thanksgiving Break (No Class) |
| Thu, Nov 28, 24 | --- |
| Fri, Nov 29, 24 | --- |
| Wed, Dec 4, 24 | Class 24: Instructor's Choice: Spatial Data, OSMNX, GeoPandas |
| Thu, Dec 5, 24 | Class 25: Wiggle Room Class / Office Hours |
| Fri, Dec 6, 24 | --- |
| Wed, Dec 11, 24 | Class 26: Final Presentations |