Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ensuring the early detection of important social network users is a challenging task. Some peer ranking services are now ll established, such as PeerIndex, Klout, or Kred. Their function is to rank users according to their influence. This notion of influence is hover abstract, and the algorithms achieving this ranking are opaque. Following the rising demand for a more transparent b,  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),  provide a precise evaluation of how topological metrics of the social network impact the final user ranking. Our approach is the following:  first model the ranking service as a black-box with which  interact by creating user profiles and by performing operations on them. Through those profiles,  trigger some slight topological modifications. By monitoring the impact of these modifications on the rankings of those profiles,  infer the ight of each topological metric in the black-box, thus reversing the service influence cookbook.
['Erwan Le Merrer', 'Gilles Tredan']
I am writing a research paper on a idea Uncovering Influence Cookbooks: Reverse Engineering the Topological Impact in Peer Ranking Services
[{"from": "human", "value": "I am writing a research paper on a idea Uncovering Influence Cookbooks: Reverse Engineering the Topological Impact in Peer Ranking Services"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ensuring the early detection of important social network users is a challenging task. Some peer ranking services are now ll established, such as PeerIndex, Klout, or Kred. Their function is to rank users according to their influence. This notion of influence is hover abstract, and the algorithms achieving this ranking are opaque. Following the rising demand for a more transparent b,  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),  provide a precise evaluation of how topological metrics of the social network impact the final user ranking. Our approach is the following:  first model the ranking service as a black-box with which  interact by creating user profiles and by performing operations on them. Through those profiles,  trigger some slight topological modifications. By monitoring the impact of these modifications on the rankings of those profiles,  infer the ight of each topological metric in the black-box, thus reversing the service influence cookbook."}]