▁are ▁themselves ▁similar . ▁The ▁base ▁case ▁is ▁that ▁objects ▁are ▁maxim ally ▁similar ▁to ▁themselves ▁. ▁ ▁It ▁is ▁important ▁to ▁note ▁that ▁Sim R ank ▁is ▁a ▁general ▁algorithm ▁that ▁determ ines ▁only ▁the ▁similarity ▁of ▁struct ural ▁context . ▁Sim R ank ▁applies ▁to ▁any ▁domain ▁where ▁there ▁are ▁enough ▁relevant ▁relationships ▁between ▁objects ▁to ▁base ▁at ▁least ▁some ▁notion ▁of ▁similarity ▁on ▁relationships . ▁Obviously , ▁similarity ▁of ▁other ▁domain - specific ▁aspects ▁are ▁important ▁as ▁well ; ▁these ▁can ▁— ▁and ▁should ▁be ▁combined ▁with ▁rel ational ▁struct ural - context ▁similarity ▁for ▁an ▁overall ▁similarity ▁measure . ▁For ▁example , ▁for ▁Web ▁pages ▁Sim R ank ▁can ▁be ▁combined ▁with ▁traditional ▁text ual ▁similarity ; ▁the ▁same ▁idea ▁applies ▁to ▁scientific ▁papers ▁or ▁other ▁document ▁corpor a . ▁For ▁recommendation ▁systems , ▁there ▁may ▁be ▁built - in ▁known ▁similar ities ▁between ▁items ▁( e . g ., ▁both ▁computers , ▁both ▁cl othing , ▁etc .), ▁as ▁well ▁as ▁similar ities ▁between ▁users ▁( e . g ., ▁same ▁gender , ▁same ▁sp ending ▁level ). ▁Again , ▁these ▁similar ities ▁can ▁be ▁combined ▁with ▁the ▁similarity ▁scores ▁that ▁are ▁computed ▁based ▁on ▁preference ▁patterns , ▁in ▁order ▁to ▁produce ▁an ▁overall ▁similarity ▁measure . ▁ ▁Basic ▁Sim R ank ▁equation ▁▁ ▁For ▁a ▁node ▁ ▁in ▁a ▁directed ▁graph , ▁we ▁denote ▁by ▁ ▁and ▁ ▁the ▁set ▁of ▁in - ne igh b ors ▁and ▁out - ne igh b ors ▁of ▁, ▁respectively