5. The method according to claim 1 , wherein the directed graph models the World Wide Web (WWW).
1. A method for computing probabilistic measures for a labeled directed graph, said method comprising: accessing, by a processor, data representative of a labelled directed graph of nodes and labels on said nodes connected by directed edges; computing, by said processor for said directed graph, flow values across all of said nodes for said labels; iteratively determining, by said processor from the directed graph, a matrix of values representative of the influence between pairs of nodes in said directed graph, wherein each entry in said matrix is determined based on said flow values, comprises a reachability measure from a first node to a second node in a given pair of nodes through all of multiple possible paths between said first node and said second node and encodes a total flow value observed by said second node when a unit of flow is sent from said first node to said second node such that said reachability measure indicates the effect that said first node has on said second node, wherein at least some of said paths have different lengths, wherein a number of iterations used, during said iteratively determining, is limited to a user-selected maximum path length to be considered, and wherein said matrix is stored as a sparse matrix in which B ij represents the total influence that node j exerts on node i, as measured by equation B=I+W+W 2 +W 3 + . . . W tmax , where I, W, W 2 , W 3 , and W tmax represent influences through paths of length zero, one, two, three, and said user-selected maximum path length, respectively; determining, by said processor from said matrix and from a distribution of a particular label in said directed graph, multiple probabilistic measures associated with said nodes and said labels of said directed graph, wherein said probabilistic measures comprise at least a measure of the importance of a node, a measure of the importance of a label, a measure of the importance of a node to a label and a measure of the importance of a label to a node; and generating rankings by said processor based on said probabilistic measures, said generating comprising at least ranking of said nodes for any given label, ranking of said labels for any given node, ranking of said labels and ranking of said nodes.