▁of ▁also ▁having ▁a ▁local ▁form ulation , ▁i . e . ▁Co Sim R ank ▁can ▁be ▁computed ▁for ▁a ▁single ▁node ▁pair . ▁Let ▁ ▁be ▁the ▁similarity ▁matrix ▁whose ▁entry ▁ ▁denotes ▁the ▁similarity ▁score ▁, ▁and ▁ ▁be ▁the ▁column ▁normal ized ▁adj ac ency ▁matrix . ▁Then , ▁in ▁matrix ▁not ations , ▁Co Sim R ank ▁can ▁be ▁form ulated ▁as : ▁ ▁where ▁ ▁is ▁an ▁identity ▁matrix . ▁To ▁compute ▁the ▁similarity ▁score ▁of ▁only ▁a ▁single ▁node ▁pair , ▁let ▁, ▁with ▁ ▁being ▁a ▁vector ▁of ▁the ▁standard ▁basis , ▁i . e ., ▁the ▁- th ▁entry ▁is ▁ 1 ▁and ▁all ▁other ▁entries ▁are ▁ 0 . ▁Then , ▁Co Sim R ank ▁can ▁be ▁computed ▁in ▁two ▁steps : ▁▁▁▁▁▁ ▁Step ▁one ▁can ▁be ▁seen ▁a ▁simplified ▁version ▁of ▁Personal ized ▁Page R ank . ▁Step ▁two ▁sums ▁up ▁the ▁vector ▁similarity ▁of ▁each ▁iteration . ▁Both , ▁matrix ▁and ▁local ▁representation , ▁compute ▁the ▁same ▁similarity ▁score . ▁Co Sim R ank ▁can ▁also ▁be ▁used ▁to ▁compute ▁the ▁similarity ▁of ▁sets ▁of ▁nodes , ▁by ▁modifying ▁. ▁ ▁Further ▁research ▁on ▁Sim R ank ▁▁▁ ▁F og ar as ▁and ▁R acz ▁ ▁suggested ▁speed ing ▁up ▁Sim R ank ▁computation ▁through ▁probabil istic ▁computation ▁using ▁the ▁Monte ▁Carlo ▁method . ▁ ▁Anton ell is ▁et ▁al . ▁extended ▁Sim R ank ▁equations ▁to ▁take ▁into ▁consideration ▁( i ) ▁evidence ▁factor ▁for ▁incident ▁nodes ▁and ▁( ii