This page attempts to describe my research activities, which currently primarily take place in the CORPNET group in Amsterdam.

Computational Network Science

Keywords: network science, corporate networks, social network analysis, computational social science, data science, algorithms

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 has two big pillars: Computational network science fundamentals and Knowledge discovery in networks:

  • Computational network science fundamentals

    I first and foremost aim to provide fundamental contributions related to the algorithms, methods and techniques used in network science, with a strong focus on contributions to computer science. This line of research continues that of my PhD thesis titled Algorithms and Analyzing and Mining Real-World Graphs (text, presentation). 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 domains. The aim is to answer important research questions from these domains by using cutting edge network science algorithms. As of September 2015, I work in 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.


Apart from papers, of which a list can be found at the Publications page, the output of my research is often a particular algorithm or piece of software. Some examples can be found below.

Computing force-directed layouts on the GPU Parallel Algorithm

Visualizing network data allows patterns and outliers in the data to be found. However, visualizing networks with hundreds of thousands or even millions of nodes, is computationally expensive. GPU's such as nVidia's Titan X can be used to speed up this progress. Together with colleague Kristian Rietveld and CS master student Govert Brinkmann we made a GPU implementation of Gephi's ForceAtlas2, a popular force-directed layout algorithm.

BoundingDiameters Algorithm

In 2011, I presented a paper that introduced an algorithm for computing the exact diameter (maximal distance) of small-world networks. Traditionally, the diameter takes O(mn) time to compute for a graph with n nodes and m edges. However, if the graph has certain properties common to small-world networks, computation can be done much more efficiently. Pruning strategies and smart lower and upper bounds together form the BoundingDiameters algorithm, which in practice finds the diameter in linear instead of quadratic time. For a network with 8 million nodes and 1 billion edges, computation time is reduced from 1.5 years to 1 minute.

teexGraph Software

teexGraph is a lightweight C++ library released in 2016 to efficiently analyze the structure of large real-world networks. It is able to analyze and summarize the network topology, but more importantly delivers maximal performance when computing distance-based metrics such as the distance distribution, center/periphery structure, and various centrality measures such as closeness centrality, betweenness centrality and PageRank. Last but not least, teexGraph includes code for a number of graph algorithms that I have devised as part of my research.

ICD - Interpretable Community Detection Software

Community detection is a well-known technique to discover groups of tightly connected nodes in networks. However, when analyzing large network datasets, it can be challenging to interpret the meaning the resulting communities. This piece of code allows one to automatically interpret the contents of communities based on some property of the nodes in these communities.


A lot of work takes place as part of a particular project.

If you are interested in finding out more about my research, then feel free to contact me!

Last modified: August 18, 2017 @ 12:32:48.