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Networks are discrete mathematical objects that describe systems of entities with pairwise relationship. Over the past several decades, technological advances in data collection and extraction have fueled an explosion of data in the form of networks from seemingly all corners of science. This course aims at providing the mathematical foundations of networks with a particular emphasis on their applications in modern data science, using tools from algorithmic graph theory and linear algebra. The topics include basic graph theory, network statistics, search algorithms, community detection, duality theorems and applications.
The course will utilize python (e.g., Networkx and Jupyter Notebook) to implement and test the techniques in graph theory and network science in synthetic and real data. Students are strongly encouraged to have some familarity in python prior to taking this course.
Much of the material covered can also be found in the following excellent texts:
Will be available
Course number: MATH 444 - Graphs and Networks in Data Science
Semester: Fall 2023
Time and place: MWF 2:25PM - 3:15PM at Van Vleck B215
Instructor: Hanbaek Lyu
Syllabus: Link (Tentative) (updated: Apr. 13, 2023)