Defence in the field of computational science, Darko Hric, M.Sc.

The title of the dissertation is: Community detection in complex networks: the role of node metadata

Darko Hric, M.Sc., will defend the dissertation "Community detection in complex networks: the role of node metadata" on 3 November 2017 at 12 noon at the Aalto University School of Science. In this dissertation, a new method that uses additional information on the nodes in complex network for clustering is proposed. This method is applied to a large network of citations between scientific journals. 

Methods to find communities in complex networks are one of many tools used in network analysis. This dissertation deals with the applicability of these methods on large and complex networks. It demonstrates that all the available information should be used, not only the connections or links between the nodes of the network, and the thesis presents one such method. This method treats the metadata on the nodes as an equivalent source of information that helps in finding the right groups of related nodes. It also shows the applicability of this method to large network of citations between scientific journals. In addition, the used state-of-the-art community detection method based on stochastic blockmodels, is shown to be able to identify groups of nodes that go beyond the traditional notion of a good community, i.e. groups of densely interlinked nodes.

The findings in this dissertation contribute to the understanding of the limits of community detection in networks, presents one possible extension that addresses one of the issues, and introduce new techniques into the field of science of science. Each of these contributions are of great interest to anyone who relies on community detection algorithms in their analysis of large, complex data.
 

Dissertation release (pdf)

Opponent: Professor Yamir Moreno, University of Zaragoza, Spain

Custos: Professor Kimmo Kaski, Aalto University School of Science, Department of Computer Science

Electronic dissertation: http://urn.fi/URN:ISBN:978-952-60-7346-0