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


Students are expected to have the following background:

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:


Lecture slides will be posted here shortly before each lecture. If you wish to view slides further in advance, refer to last year's slides, which are mostly similar.

This schedule is subject to change. All deadlines are at 11:59pm PT.

Date Description Suggested Readings / Important Notes Events Deadlines
Tue Sep 24 1. Introduction; Structure of Graphs
- Chapter 1 from Easley and Kleinberg: Overview
- Newman, Strogatz, Watts. Random graphs with arbitrary degree distributions and their applications
Homework 0 out
[bundle file]
Thu Sep 26 2. Properties of Networks
and Random Graph Models
- Chapter 20 from Easley and Kleinberg: The Small-World Phenomenon
- Watts, Strogatz. Collective dynamics of ‘small-world’ networks
- Leskovec, Chakrabarti, Kleinberg, Faloutsos, Ghahramani. Kronecker Graphs: An Approach to Modeling Networks
Homework 1 out
[bundle file]
Fri Sep 27 Recitation: Snap.py and Google Cloud tutorial
  Skilling Auditorium
Tue Oct 1 3. Motifs and Structural Roles in Networks
Milo, Shen-Orr, Itzkovitz, Kashtan, Chklovskii, Alon. Network Motifs: Simple Building Blocks of Complex Networks
- Henderson, Gallagher, Eliassi-Rad, Tong, Basu, Akoglu, Koutra, Faloutsos, Li. RolX: Structural Role Extraction & Mining in Large Graphs
Thu Oct 3 4. Community Structure in Networks
- Chapter 3 from Easley and Kleinberg: Strong and Weak Ties
- Granovetter. The Strength of Weak Ties
- Blondel, Guillaume, Lambiotte, Lefebvre. Fast unfolding of communities in large networks
- Yang, Leskovec. Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach
Homework 0 due
Fri Oct 4 Recitation: Review of Linear Algebra, Probability, and Proof Techniques
  Skilling Auditorium
Tue Oct 8 5. Spectral Clustering
Thu Oct 10 6. Message Passing and Node Classification
Homework 2 out
[bundle file]
Homework 1 due
Tue Oct 15 7. Graph Representation Learning
Thu Oct 17 8. Graph Neural Networks
Project Proposal due
Tue Oct 22 9. Graph Neural Networks:
Hands-on Session
[Colab Notebook]
Thu Oct 24 10. Deep Generative Models for Graphs
Tue Oct 29 11. Link Analysis: PageRank
Thu Oct 31 12. Network Effects and Cascading Behavior
Homework 3 out
[bundle file]
Homework 2 due
Tue Nov 5 13. Probabilistic Contagion
and Models of Influence
Thu Nov 7 14. Influence Maximization in Networks
[slides] [handout]
Project Milestone due
Tue Nov 12 15. Outbreak Detection in Networks
Thu Nov 14 16. Network Evolution
Homework 3 due
Mon Nov 18 6:30-8:30pm
  Gates 104
Alternate Exam
Tue Nov 19 7:00-9:00pm
  if SUNetID[0] in ['A', .. 'R'] then Bishop Auditorium
  if SUNetID[0] in ['S', .. 'Z'] then 420-041
  if OAE student then Gates 459
Thu Nov 21 17. Reasoning over Knowledge Graphs
Tue Nov 26 Thanksgiving
Thu Nov 28 Break        
Tue Dec 03 18. Limitations of Graph Neural Networks
Thu Dec 05 19. Applications of Graph Neural Networks
Tue Dec 10 Project Report due
Thu Dec 12 12:15-3:15pm
  Huang Foyer
Poster Session