The event is open to the public. You can attend it physically or follow it online. The link to the Zoom meeting is in the bar on the right.
ABSTRACT / Network science has become an important tool for the analysis of social systems and societies. The abstraction where these complex systems are described as graphs where two individuals are either connected or not has turned out to be incredibly versatile and useful. Methodologies developed originally within the theoretical network science community, such as network clustering and synthetic network modelling, are now routinely used to quantify complex phenomena such as political polarization and other societal phenomena. Despite this success, the simple network representation has severe limitations and investigating several salient problems can require alternative abstractions: Social groups are not just collections of pairwise connections. Discussions of polarizing topics do not live in isolation of each other. Connections and social groups are not static, but consist of temporal interactions. While until recently most of the work in network science has relied on assuming that such systems can be represented using simple graphs, there has been an increasing focus on more realistic representations of networks. The theoretical advances in multilayer networks, higher-order networks, and temporal networks allow for dealing with this need for realism in a systematic way and analyzing a multitude of different types of networks with very general mathematical and computational tools. In this talk I will go through recent work in my group related to polarization and group formation in social networks, and especially focus on our advances in using multilayer networks and other generalized network representations.
BIO / Mikko Kivelä is an assistant professor in the Department of Computer Science at Aalto University and an Academy of Finland research fellow. He received a Doctor of Science degree from Aalto University in 2012 and was a postdoc at the Mathematical Institute at University of Oxford until 2015. His research interests are in network science, with focus on generalized network representations such as temporal networks and multilayer networks.