Learning activities

1 In class activities

• Quiz: Each lecture begins with a short paper‑based quiz reviewing the previous week’s material, graded immediately when possible, followed by a discussion of common mistakes at the end of the lecture.
• Pen‑and‑Paper Exercise: Before the lecture, students complete a brief exercise to practice key concepts, then discuss solutions in class while the instructor synthesizes the answers.
• Lecture: In class lectures are delivered by the instructor.
• Network of the Week: Weekly, a student or group presents a 10‑minute paper on a network‑related topic of their choice.
• Coding: Each module includes a Python coding exercise (using Marimo notebooks) to apply the concepts to real data.

2 Homework

• Coding assignment: Every module comes with a coding assignment. The assignment will be distributed via GitHub Classroom. Students will submit their solutions to the assignment via GitHub and get automatic grading.

• LLM Quiz Challenge: Every assignment also includes a task of formulating two quiz questions and correct answers. These quiz questions will be taken by a large language model that learns the course content without seeing the correct answers. The students pass the test if they can generate questions that LLM fails to answer correctly.

3 Project

• Project Proposal: The students will submit a project proposal on the course content.
• Project Paper: The students will submit a project paper on the course content.
• Project Presentation: The students will present their project.

4 Exam

A final exam will be given at the end of the course during the exam period. This exam will be a take-home exam, and will be distributed via Brightspace.

5 Resources

• Mark Newman, Networks (Second Edition), Oxford University Press, 2018
• Filippo Menczer, Santo Fortunato, and Clayton A. Davis, A First Course in Network Science, Cambridge University Press, 2020
• James Bagrow and Yong-Yeol Ahn, Working with Network Data: A Data Science Perspective, Cambridge University Press, 2024