We study a family o f rankings\, which includes Google'\;s PageRank \, on a directed configuration model. We show tha t the the rank of a randomly chosen vertex conver ges in distribution to a finite random variable th at can be written as a linear combination of i.i. d. copies of the attracting endogenous solution t o a stochastic fixed-point equation. We provide pr ecise asymptotics for this limiting random variab le. In particular\, if the in-degree distribution in the directed configuration model has a power l aw distribution\, then the limiting distribution of the rank also follows a power law with the same exponent. Such power law behaviour of ranking is well-known from empirical studies of real-life n etworks. Our asymptotic result gives remarkably go od approximation for the complete ranking distrib ution on configuration networks of moderate size a nd on the directed graph of English Wikipedia. LOCATION:Seminar Room 1\, Newton Institute CONTACT:info@newton.ac.uk END:VEVENT END:VCALENDAR