Community Recommendation in Social Network Using Strong Friends Based on Quasi-Clique Approach

Abstract

A social networking service is a platform to build social networks or social relations among people who, share interests, activities, backgrounds or real-life connections. Social network analysis is needed because the number of users is increasing rapidly day by day. Now days, users are involved themselves in to communities. They share post, their views, what they like in communities. So it is important for them to find suitable communities where they have common factors like friends, followers and their activities etc. Here we are working with a technique for recommending a community in social network like Facebook, twitter etc. We use some graph terminologies and graph mining techniques. Finding strong friends, we recommend communities for a user in a social network. We apply data mining techniques to help social users to pick out suitable community of a social network like Facebook, twitter etc. Big social network sites use their own algorithm. Here we are not improving an existing algorithm but giving a new method for community recommendation in social network. That is workable for both real and synthetic data. As real data are not given by any big social network so we use sample data to prove our algorithm. And we make a survey and thus we improve our Community Recommendation Algorithm (CRA).