ECE 7260

Graph-based Data Science for Networked Systems

Course Description

Course information provided by the Courses of Study 2024-2025.

Complex networks are often the source of high-dimensional data. The goal of this course is to introduce structural and computational models for data that are indexed by the irregular support of a graph. The graph represents the network that couples the dynamics of many agents, or it can be a more abstract Bayesian graphical model that explains how observations are conditionally dependent. The interest in these models spans many fields.

When Offered Spring.

Comments The main background requirements are linear algebra and knowledge of probability theory. Knowledge of basic time-series/digital signals analysis is useful but not necessary.

• Students will learn to analyze graph data as networks and understand their structural features.
• Students will learn models of network dynamics that occur in science and engineering that utilize the algebra of networks.
• Students will learn techniques to analyze data that result from network dynamics in Graph Signal Processing.

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