Logistics

• Lectures: are on Tuesday/Thursday 3:00-4:20pm in the NVIDIA Auditorium.
• Lecture Videos: are available on Canvas for all the enrolled Stanford students.
• Cloud Infrastructure: this course is generously supported by Google. Each team will receive free credits to use the various Big Data and Machine Learning services offered by the Google Cloud Platform.
• Public resources: The lecture slides and assignments will be posted online as the course progresses. We are happy for anyone to use these resources, but we cannot grade the work of any students who are not officially enrolled in the class.
• Contact: Students should ask all course-related questions in the Piazza forum, where you will also find announcements. For external inquiries, personal matters, or in emergencies, you can email us at cs224w-aut1920-staff@lists.stanford.edu.
• Academic accommodations: If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE). The OAE will evaluate the request, recommend accommodations, and prepare a letter for the teaching staff. Once you receive the letter, send it to our staff email address. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations.

Content

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.

Previous Offerings

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

Prerequisites

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.

Course Materials

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