Hanjo Boekhout starts as PhD candidate

Hanjo Boekhout MSc started as a PhD candidate in the Computational Network Science group in August of 2020. Both his Bachelor’s and Master’s degree in Computer Science, with a specialization in “Computer Science and Advanced Data Analytics”, were obtained with honor (cum laude) at Leiden University. During his Master’s studies,…

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 & International Relations) are from Lehigh University. In her research,…

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 then he worked worked in high tech environments, such as…

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) package. H.D. Boekhout, W.A. Kosters and F.W. Takes, Efficiently Counting Complex Multilayer Temporal…

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 in the afternoon at TU Delft, the official opening of…

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 key players on the forum, distinguishing automatically between regular users,…

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 to the community structure of the network, into account. Thomas…