  Finding talents, often among the people already hired, is an endemic
challenge for organizations. The social networking revolution, with online
tools like Linkedin, made possible to make explicit and accessible what we
perceived, but not used, for thousands of years: the exact position and ranking
of a person in a network of professional and personal connections. To search
and mine where and how an employee is positioned on a global skill network will
enable organizations to find unpredictable sources of knowledge, innovation and
know-how. This data richness and hidden knowledge demands for a
multidimensional and multiskill approach to the network ranking problem.
Multidimensional networks are networks with multiple kinds of relations. To the
best of our knowledge, no network-based ranking algorithm is able to handle
multidimensional networks and multiple rankings over multiple attributes at the
same time. In this paper we propose such an algorithm, whose aim is to address
the node multi-ranking problem in multidimensional networks. We test our
algorithm over several real world networks, extracted from DBLP and the Enron
email corpus, and we show its usefulness in providing less trivial and more
flexible rankings than the current state of the art algorithms.
