发现影响力食谱：对等排名服务中的拓扑影响的逆向工程
Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services
摘要：
确保早期发现重要的社交网络用户是一项具有挑战性的任务。一些对等排名服务现在已经很成熟，例如PeerIndex，Klout或Kred。其功能是根据用户的影响力对用户进行排名。然而，这种影响力的概念是抽象的，实现这种排名的算法是不透明的。随着对更透明的网络的需求不断增加，我们探索了通过逆向工程获得知识的问题，这些同行排名服务，关于他们作为输入获得的社交网络拓扑结构。由于这些服务利用用户的在线活动（因此，他们在社交网络中的连接），我们提供了一个精确的评估社交网络的拓扑指标如何影响最终的用户排名。我们的方法如下：我们首先将排名服务建模为一个黑盒子，我们通过创建用户简档并对它们执行操作来与之交互。通过这些轮廓，我们触发一些轻微的拓扑修改。通过监控这些修改对这些配置文件的排名的影响，我们推断黑盒中每个拓扑度量的权重，从而扭转服务影响力食谱。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
