Keywords: network science, social network analysis, complex networks, computational social science, data science, algorithms
Computational Network Science
The over-arching theme of my research is computational network science. This means that I am interested in network science from the computational point of view. 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. My research builds around two subtopics, as described below.
- Computational network science fundamentals
I first and foremost aim to provide fundamental contributions related to the algorithms, methods and techniques used in network science, including network models, computing network metrics centrality, community detection and motif detection. This line of research continues that of my PhD thesis titled Algorithms and Analyzing and Mining Real-World Graphs (text, presentation). Other topics include: computational aspects of algorithms for analyzing large-scale network data, network visualization, high performance network data processing and large-scale network data management.
- Knowledge discovery in networks
The second pillar of my research deals with analyzing, visualizing and mining large-scale network data from a range of application domains. The aim is to answer important research questions from these domains by using cutting edge network science algorithms. Since September 2015, I am closely affiliated with the CORPNET project at the University of Amsterdam, where I focus on network-related questions on corporate governance as seen from the domain of political economy. In particular, I work on analyzing corporate and economic networks in which firms are connected based on for example interlocking directorates and ownership. Other applications are often addressed through specific projects.
Sometimes, a project or paper results in a piece of hopefully reusable software, e.g.:
- Computing force-directed layouts on the GPU
- BoundingDiameters algorithm for computing the diameter of a small world network
- The teexGraph software package for structural network analysis at scale
- ICD – Interpretable Community Detection; a script for interpreting large-scale community detection results
- Multiplex network motif recognition; a multiplex adaptation of algorithms for finding frequent subgraphs