Machine Learning on Networks

Networks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. Characteristic examples include social networks (e.g., Facebook, Twitter), information networks (e.g., the Web) as well as technological networks (e.g., the Internet). The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and learning methods constitute prominent tools. The goal of this course is to present recent and state-of-the-art methods and algorithms for analyzing, mining and learning large-scale network data, as well as their practical applications in various domains (e.g., the web, social networks, recommender systems).

Class Timetable

 Start TimeEnd TimeProfessor
July 8, 20199:30 am12:00 pm Fragkiskos Malliaros
July 8, 20191:30 pm4:00 pm Fragkiskos Malliaros
July 8, 2019
9:30 am - 12:00 pm - with Fragkiskos Malliaros - at
July 8, 2019
1:30 pm - 4:00 pm - with Fragkiskos Malliaros - at