PageRank assigns authority weights to each web page based on the web hyperlink structure,while the personalized PageRank is a generalized version of ordinary PageRank.The computation of personalized PageRank vector in unweighted web is well studied in the past decades,but little is known for the case of weighted webs.In this paper,we analyze the algorithms for PageRank computations in static as well as dynamic weighted networks.The algorithms are based on matrix transformation or Monte Carlo methods,and are analyzed theoretically for computation performance.Experiments show that the proposed localized algorithm outperforms power iteration and a referenced Monte Carlo method.