Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. Hover, in practice, not all users provide these attributes.   ,  ask the question: given attributes for some fraction of the users in an online social network, can  infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To ansr this question,  gather fine-grained data from two social networks and try to infer user profile attributes.  find that users with common attributes are more likely to be friends and often form dense communities, and  propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users.
['Alan Mislove', 'Bimal Viswanath', 'Krishna P. Gummadi', 'Peter Druschel']
I am writing a research paper on a idea You are who you know: inferring user profiles in online social networks
[{"from": "human", "value": "I am writing a research paper on a idea You are who you know: inferring user profiles in online social networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. Hover, in practice, not all users provide these attributes.   ,  ask the question: given attributes for some fraction of the users in an online social network, can  infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To ansr this question,  gather fine-grained data from two social networks and try to infer user profile attributes.  find that users with common attributes are more likely to be friends and often form dense communities, and  propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users."}]