finding community base on web graph clustering

search pointers organize the main part of the application on the internet. however, because of information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. so the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. community (web communities) can be used to generate automated directory services. in this paper the act of clustering has been done by finding the complete bipartite sub- graphs. the sub- graphs form the core of a community or clustering and by extending the core we can attain to the whole clustering .the whole set of graphs in england are 18 million pages and 300 million links