How do diseases and information spread? Who are the influencers? Can we predict friendships in a social network? Networks are the core of the WWW, 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. This course will focus on how to analyze the structure and dynamics of large networks, how to model links, and how design algorithms that work with such large networks.
- Statistical properties of large networks
- Models of social network structure and evolution
- Link prediction
- Network community detection
- Diffusion of innovation
- Information propagation
- Finding influential nodes in networks
- Disease outbreak detection
- Networks with positive and negative ties
- Connections with work in the social sciences and economics
- Jure Leskovec Assistant Professor of Computer Science
3.0 - 4.0
Students enrolling under the non degree option are required to take the course for 4.0 units.
- Homework(4)- 50%
- Final Project- 50%
The Final Project consists of a project proposal (20%), a project milestone report (15%), a final project report (50%), and a poster presentation (15%).
- 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)
We highly recommend watching the course preview to ensure you have the requisite background and understand the scope of material covered.
Tuition & Fees
For course tuition, reduced tuition (SCPD member companies and United States Armed forces), and fees, please click Tuition & Fees.