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,
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