Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services

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 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.