Fundamentals and Applications of Graph Neural Networks. 3 credits, 3 contact hours

Catalog description

Prerequisites: ( CS 100 or DS 100 ) and CS 375 . Graphs provide a natural framework for representing complex relationships between various objects. Graph Neural Networks (GNNs) have gained significant importance in both academic research and industrial applications. This course introduces GNNs and explores foundational concepts, algorithms, and diverse applications. Students will learn the fundamentals of graph theory, and key models, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), advanced graph diffusion models, and integrations of GNNs with sequential models for temporal graph modeling. The course will cover practical applications across fields like social networks, biological networks, brain networks, and finance, focusing on hands-on implementation and problem-solving. By the end of the semester, students will be skilled in designing and applying GNN models to real-world datasets.