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.

Probability and statistics, linear algebra, machine learning, deep learning

taking this class for S/U grading is not allowed due to the team project.
- Midterm
- Final project
- Final presentation

- Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg
- Network Science by Albert-László Barabási
- CS224W by Stanford

- Structure of Graphs
- Properties of Networks and Random Graph Models
- Motifs and Structural Roles in Networks
- Community Structure in Networks
- Spectral Clustering
- Message Passing and Node Classification
- Graph Representation Learning
- Graph Neural Networks
- Deep Generative Models for Graphs
- Network Effects and Cascading Behavior
- Probabilistic Contagion and Models of Influence
- Influence Maximization in Networks
- Outbreak Detection in Networks
- Network Evolution
- Reasoning over Knowledge Graphs

- Offline lectures