While the emergence of a power law degree distribution in complex networks is
intriguing, the degree exponent is not universal. Here we show that the
betweenness centrality displays a power-law distribution with an exponent \eta
which is robust and use it to classify the scale-free networks. We have
observed two universality classes with \eta \approx 2.2(1) and 2.0,
respectively. Real world networks for the former are the protein interaction
networks, the metabolic networks for eukaryotes and bacteria, and the
co-authorship network, and those for the latter one are the Internet, the
world-wide web, and the metabolic networks for archaea. Distinct features of
the mass-distance relation, generic topology of geodesics and resilience under
attack of the two classes are identified. Various model networks also belong to
either of the two classes while their degree exponents are tunable.Comment: 6 Pages, 6 Figures, 1 tabl