Network science

scale-free network - node degree distribution - power law
The research of complex networks and systems

The analysis of networks has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings, popularized by Albert-László Barabási and his team, is that real networks behave very distinct from traditional assumptions of network theory.

Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modeled by random graphs. But modern network research could show that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). This power-law (scale-free) characteristics can be found in many real networks from biological to social networks.

However, it turns out that power-law (scale-free) node-degree distributions are a property of only sparsely connected networks. More densely connected networks show an increasing divergence from power-law, read more..







Data sets

Graph drawing tools & Network analysis software

Open positions

Matthias Scholz