Course overview
Course description
Network science is a unifying framework to study complex systems, such as living organisms, societies, and many techno-social systems. Therefore, understanding networks and network (graph) data is fundamental to numerous domains. This graduate-level course introduces the fundamental concepts as well as key applications of network science for a broad range of students. Topics include statistical properties and models of real-world networks, network data & algorithms, how information and diseases spread in our society, and machine learning with networks — e.g., community detection (clustering) and graph embedding.
Because your learning should be the primary focus, please engage actively by voicing your confusions, challenges, and intriguing digressions! Rather than merely watching lectures, think critically, engage in debates, and immerse yourself.
Course objectives
By the end of the course, students are expected to be able to identify, construct, model, and analyze networks by choosing and applying appropriate methods and algorithms, as well as understanding ethical issues surrounding network data. Students are also expected to be able to explain, both mathematically and conceptually, the key network concepts, algorithms, models, and statistical properties, as well as their implications.
Basic Information
- Homepage: https://yy.github.io/netsci-course/
- GitHub: https://github.com/yy/netsci-course
- Instructor: Yong-Yeol (YY) Ahn
- Announcements: All announcements will be sent via Canvas and Slack.
- Syllabus: You can download the syllabus here.
- Textbook: We will primarily use Working with Network Data (WWND) by James Bagrow and yours truly (Cambridge University Press, in press). I also use some chapters from the following books (they are all great):
- Network Science by Albert-László Barabási (Cambridge University Press, 2016).
- Networks: An Introduction by Mark Newman (Oxford University Press, 2018).
- Networks, Crowds, and Markets by David Easley and Jon Kleinberg (Cambridge University Press, 2010).
Links
Special Thanks
- Francisco Alfaro helped the migration with mkdocs.