Computer Science Colloquium: Data Science for Networks

Wednesday, January 08, 2020, 2:00pm

Location: Computer Science Center, E3, Room 9222

Speaker: Michael Schaub (University of Oxford, UK and MIT, USA)

Abstract:

Networks have become a widely adopted model for a range of systems, cutting across Science and Engineering. However, our theoretical understanding of many fundamental phenomena that arise in complex networks and networked systems is still limited. My vision is to develop a data science for networks and dynamical systems that will contribute to addressing this challenge, by combining data-driven and model-based approaches, using the language of graphs and networks.

In this talk, we will give a brief overview of such a Data Science for Networks. We first discuss how networks appear naturally within models in different research domains and illustrate the underlying scientific questions via examples drawn from applications. We then examine in some detail the problem of feature learning from graphs with unobserved edges, in which we aim to learn certain aspects of a graph solely from dynamical observations on its nodes, without knowledge of the edge-set of the graph. We conclude with a brief outlook on future challenges and open problems.

 

The computer science lecturers invite interested people to join.