Data-driven Network Science

About

Networks are an important data representation vehicle. There is a long tradition in social sciences concerning social network analysis, but over the last 20 years alternative network analysis methods have been developed under a complex systems approach.

Data-driven Network Science will introduce the students to network summaries, network models and network algorithms. Different methods for analysing network data will be presented; these include network centrality, community detection and dynamical processes on networks.

Key Themes

• Node centrality
• Models for networks
• Community detection
• Impact of structure on diffusive processes
• Signed networks

Learning Objectives

At the end of this course students will understand state-of-the-art algorithms for network mining know how to formulate scientific questions related to network data know how to perform numerical experiments and statistical tests be able to report on the analysis of network data in a critical way.