COMP 150-02 Machine Learning for Graph Data Analytics

Graph and network data are ubiquitous and often in large scale. Graph data are generally characterized by the graph structure and data attached to graph nodes or edges. Machine learning is an important approach to automated information extraction from graph data. As most learning models (e.g. neural networks) only accept vectors as the input, graph data need special model designs. Existing models generally fall into two categories: 1) models that learn vector representations of graph data, and 2) models that take graphs as the input. In this course, we will start with a shallow introduction of graph theory and then discuss a series of learning methods for graphs. The topics include graph embedding, generative models, graph neural networks, kernel methods, and a few applications. The course work consists of 2 to 3 projects and a final project.

Approved as a category 2 elective in Data Science (analysis and interfaces).