The structure of criminal discussion forum networks on the dark net
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 […]
A community-aware approach for detecting network anomalies
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 […]
Counting temporal motifs in multilayer networks
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 […]
Using network analysis to understand the role of software components
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 […]
New CNS Lab website
This is the very first post on the new Leiden CNS Lab website. In the future, this website will be updated with news and information on upcoming events and activities.
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.
