  It has been shown that many networks associated with complex systems are
small-world (they have both a large local clustering coefficient and a small
diameter) and they are also scale-free (the degrees are distributed according
to a power law). Moreover, these networks are very often hierarchical, as they
describe the modularity of the systems that are modeled. Most of the studies
for complex networks are based on stochastic methods. However, a deterministic
method, with an exact determination of the main relevant parameters of the
networks, has proven useful. Indeed, this approach complements and enhances the
probabilistic and simulation techniques and, therefore, it provides a better
understanding of the systems modeled. In this paper we find the radius,
diameter, clustering coefficient and degree distribution of a generic family of
deterministic hierarchical small-world scale-free networks that has been
considered for modeling real-life complex systems.
