Online　rating　data　is　ubiquitous　on　existing　popular　E-commerce　websites　such　as　Amazon,　Yelp　etc.,　which　influences　deeply　the　following　customer　choices　about　products　used　by　E-businessman.　Collaborative　filtering　recommender　systems　(CFRSs)　play　crucial　role　in　rating　systems.　Since　CFRSs　are　highly　vulnerable　to　＂shilling＂　attacks,　it　is　common　occurrence　that　attackers　contaminate　the　rating　systems　with　malicious　rates　to　achieve　their　attack　intentions.　Despite　detection　methods　based　on　such　attacks　have　received　much　attention,　the　problem　of　detection　accuracy　remains　largely　unsolved.　Moreover,　few　can　scale　up　to　handle　large　networks.　This　paper　proposes　a　fast　and　effective　detection　method　which　combines　two　stages　to　find　out　abnormal　users.　Firstly,　the　manuscript　employs　a　graph　mining　method　to　spot　automatically　suspicious　nodes　in　a　constructed　graph　with　millions　of　nodes.　And　then,　this　manuscript　continue　to　determine　abnormal　users　by　exploiting　suspected　target　items　based　on　the　result　of　first　stage.　Experiments　evaluate　the　effectiveness　of　the　method.　Copyright　©　2014　IGI　Global.