The Leiden Computational Network Science (CNS) group works on methods and algorithms for knowledge discovery from real-world network data. Using a combination of graph algorithms and machine learning techniques, we strive to unveil patterns in dynamic complex networks from a range of application domains. Examples include social networks, communication networks, scientific networks, infrastructure networks and corporate/economic/financial networks.

Network science can be seen as a specialization of data science that focuses on network data, or alternatively, as a particular method in complexity research. Typically the network perspective reveals patterns and emerging phenomena that are not visible when the mere individual objects in the data are studied. Sometimes, this field is also referred to as social network analysis or complex networks.

About the group. The CNS group is lead by Frank Takes and is part of the Department of Computer Science (LIACS) at Leiden University, as a member of the Data Science cluster, but also affiliating with the Theory cluster. The group is associated locally with the Leiden Complex Networks Network (LCN2) and nationally with the Dutch Chapter of the Network Science (NL NetSci) Society.

Projects and collaborations. The CNS group hosts a number of research projects by its PIs, and features several multidisciplinary collaborations, involving other scientific disciplines such as economics, law, archaeology and the social sciences. Important (past) collaborations include the CORPNET group and computational social science (CSS) platform of the University of Amsterdam. And more recently, the inter-university POPNET project. Various partners in industry and the public sector, such as Statistics Netherlands (CBS) are also frequently involved.

Teaching. The members of the group are also responsible for the MSc course Social Network Analysis for Computer Scientists and contribute to the BSc/MSc Complex Networks course.