Social network sites have attracted millions of users with the social revolution in Web2.0. Asocial network is composed by communities of individuals or organizations that are connectedby a common interest. Online social networking sites like Twitter, Facebook and Orkut areamong the most visited sites in the Internet chew, (2008). In the social network sites, a user canregister other users as friends and enjoy communication. However, the large amount of onlineusers and their diverse and dynamic interests possess great challenges to support such a novelfeature in online social networks kwon, (2010). In this work, we design a general friendrecommendation framework based on cohesion after analyzing the current method of friendrecommendation. The main idea of the proposed method is consisted of the following stagesmeasuringthe link strength in a network and find out possible link on this network that is yet tobe established; detecting communities among the network using modularity and recommendingfriends. Considering the noticeable attraction of users to social networking sites, lots ofresearch has been carried out to take advantage of the users ‘information available in thesesites. Knowledge mining techniques have been developed in order to extract valuable pieces ofinformation from the users’ activities. This paper deals with a methodology to generate a socialgraph of users’ actions and predict the future social activities of the users based upon theexisting relationships. This graph is updated dynamically based on the changes in the selectedsocial network. The forecasting performed is based upon some predefined rules applied on thegraph.
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