Advanced Topics in Network Science: Graph Learning

Graph representations of relational data are crucial for data science and machine learning across scientific fields. Graph mining and learning techniques help detect functional modules in biological networks, find communities and missing links in social networks, and perform node-, link-, or graph-level classification. This course equips students with techniques for supervised and unsupervised learning on complex networks. We explore statistical learning methods to infer clusters and predict links, introduce approaches to learn low-dimensional vector representations of graphs, and discuss deep learning applications to complex networks.