Machine Learning for Dynamic Social Network Analysis

Basic details

In recent years, there has been an increasing effort on developing realistic representations and models as well as learning, inference and control algorithms to understand, predict, and control dynamic processes over social and information networks. This has been in part due to the increasing availability and granularity of large-scale social activity data, which allows for data-driven approaches with unprecedented accuracy.

In this seminar, you will first learn how to utilize the theory of temporal point processes to create realistic representations and models for a wide variety of dynamic processes in social and information networks. Then, you will get introduced to several inference and control problems of practical importance in the context of dynamic processes over networks, and learn about state-of-the-art machine learning algorithms to solve these problems.

Jan 25 '17 — Jan 30 '17
10:30 — 12:30
Access Grid Room (Carslay Building, level 8 room 829)


25th January, 2017
Representation: temporal point processes
27th January, 2017
Applications: Models
30th January, 2017
Applications: Control