Advanced Social Computing (COMP5800)

This course introduces the background and current states of social networks and their analysis in terms of content, users, social relations and applications, and covers the fundamentals of graph and text processing. The course has a particular emphasis on key advancements in graph and text data processing - with bias toward the latter, reflecting instructor biases. At the end of this course, students should have good understanding of the background, design, analysis and implementation of social computing systems, as well as hands-on experience on a range of tasks from identifying important nodes to detecting communities to tracing information diffusion in social networks. Special emphasis will be given to understanding ML and NLP techniques and using them in practice. Guest lectures by distinguished researchers and course projects emphasize subtleties of translating ML and NLP into practical applications in social networks. In order to succeed in this course, students should have a strong interest in conducting (or learning how to conduct) research. Prior exposure to ML or (statistical) NLP is recommended but not strictly required. Familiarity with linear algebra, (basic) calculus, and probability will be assumed throughout the course.