Quantitative Issues in Cancer Research Working Group Seminar

Elizabeth Graff, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Applications of Deep Learning for Graph-Structured Data: From Disease Spread to Social Networks

Abstract: How can we apply deep learning to solve problems in modeling the spread of disease? In this talk, we will explore the components and applications of Graph Neural Networks (GNNs), a class of neural networks that are specifically designed to learn from graph-structured data. We will discuss the versatility of graphs in representing complex relationships across various domains from molecular structures to social networks, which necessitates models like GNNs that can capture both the graph topology and node-level information. We will examine examples of studies that leverage GNNs to achieve machine learning tasks on graphs, including node and edge predictions and whole graph classifications.