This is an inactive course webpage.
Lectures: TUE/THU 10-11:20am in Seigle 103
Instructor: Marion Neumann
Office: Jolley Hall Room 222
Contact: Please use Piazza!
Office Hours: TUE 11:30am-12:30pm or individual appointment (request via email – allow for 1-2 days to reply and schedule)
Please, avoid random drop ins outside my office hours.
Assistant to the Instructor: Raj (office: Jolley 217B)
TAs: Josh (Feiyang), Sam C, Sam G,
TA/AI Office Hours:
MON 3-5pm (Josh, Sam G) in Crow 204 WED 5:30-7:30 (Sam C, Sam G) in Eads 216 FRI 2-4 (Raj) in Lopata 202
We will use the representative power of graphs to model networks of social, technological, or biological interactions. Network analysis provides many computational, algorithmic, and modeling challenges. We begin by studying graph theory, allowing us to quantify the structure and interactions of social and other networks. We will then explore how to practically analyze network data and how to reason about it through mathematical models of network structure and evolution. Another main objective will be to investigate algorithms that extract basic properties of networks in order to find communities and infer node properties. Finally, we will study a range of applications including robustness and fragility of networks such as the internet, spreading processes used to study epidemiology or viral marketing, and the ranking of webpages based on the structure of the webgraph.
This course combines concepts from computer science and applied mathematics (matrix algebra and optimization) to study networked systems using data mining.
Prerequisites: CSE 240 (discrete maths/proofs), CSE 247, ESE 326 (prob/stats), MATH 309 (matrix algebra), and programming experience (note: you will need to write programs to parse data and analyze networks using Python)
Please ask any questions related to the course materials and homework problems on Piazza. Other students might have the same questions or are able to provide a quick answer.
Any public postings of (partial or full) solutions to homework problems (written or in form of source or pseudo code) will result in a grade of zero for that particular problem for ALL students in the course.