  Ensuring the early detection of important social network users is a
challenging task. Some peer ranking services are now well established, such as
PeerIndex, Klout, or Kred. Their function is to rank users according to their
influence. This notion of influence is however abstract, and the algorithms
achieving this ranking are opaque. Following the rising demand for a more
transparent web, we explore the problem of gaining knowledge by reverse
engineering such peer ranking services, with regards to the social network
topology they get as an input. Since these services exploit the online activity
of users (and therefore their connectivity in social networks), we provide a
precise evaluation of how topological metrics of the social network impact the
final user ranking. Our approach is the following : we first model the ranking
service as a black-box with which we interact by creating user profiles and by
performing operations on them. Through those profiles, we trigger some slight
topological modifications. By monitoring the impact of these modifications on
the rankings of those profiles, we infer the weight of each topological metric
in the black-box, thus reversing the service influence cookbook.
