GRAPHS AND NETWORKS IN DATA SCIENCE

Prerequisites & course information

Requisites: ( MATH 320 , 340 , 341 , 345 , or 375 ) and ( COMP SCI 200 , 220 , 300 , 310 , or placement into COMP SCI 300 ), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate) Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. State formal definitions in graph theory and networks. Audience: Both Grad & Undergrad 2. Describe essential network search algorithms (e.g., shortest path and minimum spanning tree) and prove their correctness and computational complexity. Audience: Both Grad & Undergrad 3. Understand how community detection algorithms using spectral analysis works; implement and/or use community detection algorithms and apply them to real-world networks using Python. Audience: Both Grad & Undergrad 4. Simulate various random graph models (Erdos-Renyi, Watts-Strogats, Stochastic Block Model, Configuration model, Preferential Attachment). Audience: Both Grad & Undergrad 5. Use a programming language to load network data and apply deterministic and randomized algorithms for network data analysis; fit various random graph models to the given real-world network and infer its statistical properties. Audience: Both Grad & Undergrad 6. Identify applications of course content in areas of modern research. Audience: Graduate Learning Outcomes: 1. State formal definitions in graph theory and networks. Audience: Both Grad & Undergrad 2. Describe essential network search algorithms (e.g., shortest path and minimum spanning tree) and prove their correctness and computational complexity. Audience: Both Grad & Undergrad 3. Understand how community detection algorithms using spectral analysis works; implement and/or use community detection algorithms and apply them to real-world networks using Python. Audience: Both Grad & Undergrad 4. Simulate various random graph models (Erdos-Renyi, Watts-Strogats, Stochastic Block Model, Configuration model, Preferential Attachment). Audience: Both Grad & Undergrad 5. Use a programming language to load network data and apply deterministic and randomized algorithms for network data analysis; fit various random graph models to the given real-world network and infer its statistical properties. Audience: Both Grad & Undergrad 6. Identify applications of course content in areas of modern research. Audience: Graduate