Figure: Sample of an online social network

The Leiden Computational Network Science Lab (CNS Lab) researches methods 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 lab. The CNS Lab is lead by Frank Takes and is part of the Department of Computer Science (LIACS) at Leiden University, affiliating with both the Theory and Data Science clusters. The lab is affiliated locally with the Leiden Complex Networks Network (LCN2) and nationally with the Dutch Chapter of the Network Science (NL NetSci) Society. The CNS Lab is a result of the bundling of resources from a number of granted projects. It features collaborations with other projects and initiatives, such as the Leiden Center of Data Science (LCDS) and the Netherlands eScience Center (NLeSC). Research often occurs in a multidisciplinary setting, involving other scientific disciplines such as economics, law, archaeology and the social sciences, in particular the CORPNET group and computational social science (CSS) platform of the University of Amsterdam. Various partners in industry and the public sector are also involved.