  Hierarchical organization is an important, prevalent characteristic of
complex systems; in order to understand their organization, the study of the
underlying (generally complex) networks that describe the interactions between
their constituents plays a central role. Numerous previous works have shown
that many real-world networks in social, biologic and technical systems present
hierarchical organization, often in the form of a hierarchy of community
structures. Many artificial benchmark graphs have been proposed in order to
test different community detection methods, but no benchmark has been developed
to throughly test the detection of hierarchical community structures. In this
study, we fill this vacancy by extending the Lancichinetti-Fortunato-Radicchi
(LFR) ensemble of benchmark graphs, adopting the rule of constructing
hierarchical networks proposed by Ravasz and Barab\'asi. We employ this
benchmark to test three of the most popular community detection algorithms, and
quantify their accuracy using the traditional Mutual Information and the
recently introduced Hierarchical Mutual Information. The results indicate that
the Ravasz-Barab\'asi-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark
generates a complex hierarchical structure constituting a challenging benchmark
for the considered community detection methods.
