Machine Learning with Graphs
Description
How do diseases and information spread? Who are the influencers? Can we predict friendships in a social network? Networks are the core of the internet, blogs, Twitter and Facebook. They can be characterized by the complex interplay between information content, millions of individuals and organizations that create it, and the technology that supports it. The course will cover recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Students will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution.
Prerequisites
- Knowledge of basic computer science principles at a level sufficient to write a reasonably non-trivial computer program. (e.g., CS107, CS145 or equivalent are recommended)
- Familiarity with the basic probability theory. (CS109 or STATS116 is sufficient but not necessary)
- Familiarity with the basic linear algebra. (CS205 would be much more than necessary)
Topics include
- Models of the small world and decentralized search
- Search in P2P networks and strength of weak ties
- Graph structure of the web
- Models of network evolution and network cascades
- Influence maximization in networks
- Communities and clusters in networks
- Link analysis for networks
- Networks with positive and negative edges
Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.
