The theme of my research group is computational network science. The main idea behind network science is that by investigating interactions between objects in a dataset as a network, we can better understand the considered data. Moreover, from the network perspective we are often able to reveal patterns and phenomena that are not visible when the mere individual objects are studied. One could see network science as a specialization of data science that focuses on network data. At the same time, network science can be seen as a particular method in complexity research. It is also referred to as social network analysis or complex network analysis. The group’s research builds around two subtopics:
- Computational network science fundamentals
This concerns fundamental contributions related to the algorithms, methods and techniques used in network science, including network models, computing network measures such as the diameter and node centrality, community detection, motif recognition, network visualization, high performance network data processing and large-scale network data management.
- Computational social science
This part of our work can be seen as the process of knowledge discovery in networks. Analyzing, visualizing and mining large-scale network data from a range of application domains, in particular the social sciences and economics, is considered. The aim is to answer important research questions from these domains by using cutting edge network science methods.
To get an idea of what kind of research all of the above entails, find more information at the website of the Leiden Computational Network Science Lab.
Keywords: network science, social network analysis, complex networks, computational social science, data science, (graph) algorithms