Sparse Social Domains Based Scalable Learning of Collective Behaviour

Abstract-Social networking is process where many people get connected with each other share their views and images. Social Networking has become very important these days where many people get connected globally, every individual today has an social networking site account for example we can consider Facebook which has gained a lot of importance when compared to other social networking sites. We have many social networking domains available in the market like Facebook, Twitter, Linkedin and many others. Social Network is good and interesting at the other side it is insecure also. Now a day's social network accounts are hacked so it is very important for every individual to logout properly in the system where they have used the network and also they should not share their account details with anyone which may lead to illegal issues. In this paper we are performing a scalable learning of a particular user through the usage of their social network and also giving a report like the main purpose for which the social network site was used by that user. Apart from the scalable learning we are also checking with the access control in the social networks where a user can share their views or images or videos to a specific group or to friends secretly. As the social network has gained more significance every individual is curious to get more likes to their posts so it is a very important task to stop the fake accounts or detect the Sybil users in the network. This paper does three tasks in total which are scalable learning, sharing access rights and detection of fake accounts