In a new paper titled “Efficiently counting complex multilayer temporal motifs in large-scale networks” we detail our approach to counting multilayer temporal motifs in networks with partial timing. The code (see Bitbucket) builds upon the well-known Stanford Network Analysis (SNAP) […]
At the 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), as part of the MSNDS workshop Leiden CNS Lab student Hanjo Boekhout will present the following paper about fast computation of closeness centrality in […]
On October 2, 2019, sunny Santa Clara, California (US) will be the scene for the 3rd International Workshop on Mining Actionable Insights from Social Networks (MAISoN). We welcome original submissions on topics related to social network mining and related fields. […]
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 […]
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 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. The CNS Lab is lead by Frank Takes and is part of the Theory and Data Science clusters of the Department of Computer Science (LIACS) of Leiden University.