Complex Networks
Network science 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 characteristics, termed scale-free by Barabási, can be found in many complex real networks from biological to social networks.
But such scale-free power-law node-degree distributions are restricted to the typically observed sparsely connected networks. More densely connected networks show an increasing divergence from power-law. This is consistent with the classic idea from social sciences that similarity is the driving factor behind communication in social networks (read more).
