An adaptive search engine considering user´s community

Search engines are broadly used by people who take part in different social networks. Information in social networks can be used to optimize search results. Friendship links in a social network creates a graph of communities. A community in network contains users that are like one another and unlike nodes containing in the further communities. Chronically, similar vertices have common neighbors. In a social network if two users have lots of common friends, they possibly share an interest. As a result, they search for common information and one user can gain profit from the other´s search process. In this paper, we try to optimize search engine process. We consider the visit rate of Web pages by community-mates (users in same community) as a criterion to rank the search results. Experimental results show that more relevant page results are presented in higher ranks by our search engine.