Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services
Erwan Le Merrer, Gilles Tr\'edan

TL;DR
This paper investigates how peer ranking services determine user influence by reverse engineering their algorithms through topological network analysis, revealing the impact of social network structure on influence scores.
Contribution
It introduces a black-box approach to reverse engineer influence algorithms by analyzing the effect of topological modifications on user rankings.
Findings
Topological metrics significantly affect influence rankings.
The method effectively infers the importance of different network features.
Reveals the opaque influence mechanisms of peer ranking services.
Abstract
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Recommender Systems and Techniques · Internet Traffic Analysis and Secure E-voting
