What is this course about? [Info Handout]
Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with the emergence of online social networks and large-scale data availability in biological sciences, this course focuses on the analysis of massive
networks which provide several computational, algorithmic, and modeling challenges. Students are introduced to machine learning techniques and data mining tools apt to reveal insights on the social, technological, and natural worlds, by
means of studying their underlying network structure and interconnections.
Topics include: robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; graph neural networks and representation learning; identification of functional modules in biological networks; disease outbreak detection.
You can access slides and project reports of previous versions of the course on our archived websites: CS224W: Fall 2018 / CS224W: Fall 2017 / CS224W: Fall 2016 / CS224W: Fall 2015 / CS224W: Fall 2014 / CS224W: Fall 2013 / CS224W: Fall 2012 / CS224W: Fall 2011 / CS224W: Fall 2010
Students are expected to have the following background:
- Knowledge of basic computer science principles, sufficient to write a reasonably non-trivial computer program (e.g., CS107 or CS145 or equivalent are recommended)
- Familiarity with the basic probability theory (CS109 or Stat116 are sufficient but not necessary)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary)
The recitation sessions in the first weeks of the class will give an overview of the expected background.
Notes and reading assignments will be posted periodically on the course Web site. The following books are recommended as optional reading:
- Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg
- Network Science by Albert-László Barabási