Data traffic for clouds for complex networks

Analyzing large-scale complex networks

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.