Carolina Mattsson starts as postdoc
dr. Carolina Mattsson started as a postdoctoral researcher in the Computational Network Science group in May of 2020. She holds a doctorate in Network Science from Northeastern University, where she was an NSF Graduate Research Fellow. Her undergraduate degrees (Physics […]
4th Workshop on Mining Actionable Insights from Social Networks at TheWebConf 2020 in Taiwan
The 4th International workshop on Mining Actionable Insights from Social Networks (MAISoN 2020) takes place on April 20 and is co-located with The Web Conference 2020 (formerly WWW), which takes place from April 20 to 24, 2020 in Taipei, Taiwan. […]
Andre Beijen starts as PhD candidate
Andre Beijen started his PhD project in the fall of 2019 in the Computational Network Science group at LIACS, Leiden University. Andre has a degree in applied physics from Technical University Delft, where he graduated in statistical signal processing. Since […]
Efficiently counting complex multilayer temporal motifs in large-scale networks
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) […]
Fast Incremental Computation of Harmonic Closeness Centrality in Directed Weighted Networks
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 […]
3rd International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2019)
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. […]
Opening Symposium of the Dutch Network Science Society on May 7
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 […]
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 […]
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.
