The Dutch Network Science Society is a society aimed at bringing together researchers in the Netherlands working on the topic of network science; the extraction of knowledge and insights from theoretical and empirical analysis of complex networks. On May 7 […]
As part of his MSc in Computer Science with Data Science specialization, Alain Fonhof analyzed real-world network data sets collected from online discussion forums on the dark net. It turns out that network centrality measures can succesfully identify and characterize […]
Network anomalies can for for example be spammers in communication networks, intruders in physical network systems or bots on social media. Master student Thomas Helling developed a new algorithm for finding such spammers, taking the awareness of nodes with respect […]
Network motifs are small building blocks consisting of a handful of nodes. Complex networks are made up out of countless of these little network patterns. Master student Hanjo Boekhout worked on algorithms for efficiently counting these motifs in a large-scale […]
Software typically consists of a large number of components (in software design terms called ‘classes’). Master student Xavyr Rademaker worked on new ways of automatically determining the role of such a software component using a combination of machine learning and […]
About the CNS Lab
The Leiden Computational Network Science Lab (CNS Lab) researches algorithms and methods for knowledge discovery from real-world network data. Using both computational and machine learning methods, the goal is to unveil patterns in dynamic complex networks from a range of application domains. The CNS Lab is lead by Frank Takes and is part of the Department of Computer Science (LIACS) of Leiden University.