The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative “importance” of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accuratel...

As data grows in size, search engines face new challenges extracting more relevant content for users’ searches. a result, number of retrieval and ranking algorithms have been employed to ensure that the results are user’s requirements. Unfortunately, most existing indexes crawl documents web pages based on limited set criteria designed meet user expectations, making it impossible deliver except...

In 1999, Page et al. described their algorithm — PageRank — for scoring pages in their web search engine, Google. Already from the start it was clear that due to the large number of pages to be ranked, the PageRank algorithm’s efficiency and optimal performance was and is a critical feature. One method of increasing the efficiency of calculations is based on MapReduce, also originally published...

In this paper we explore the use of ranking as a mean of guiding unsupervised image segmentation. Starting by the well known Pagerank algorithm we introduce an extension based on quantum walks. Pagerank (rank) can be used to prioritize the merging of segments embedded in uniform regions (parts of the image with roughly similar appearance statistics). Quantum Pagerank, on the other hand, gives h...

Personalized PageRank is an algorithm to classify the improtance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with nodedependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the probability of visited node just before the restart. In the origi...

It is known that the output from Google’s PageRank algorithm may be interpreted as (a) the limiting value of a linear recurrence relation that is motivated by interpreting links as votes of confidence, and (b) the invariant measure of a teleporting random walk that follows links except for occasional uniform jumps. Here, we show that, for a sufficiently frequent jump rate, the PageRank score ma...

This note extends the analysis of incremental PageRank in [B. Bahmani, A. Chowdhury, and A. Goel. Fast Incremental and Personalized PageRank. VLDB 2011]. In that work, the authors prove a running time of O( 2 ln(m)) to keep PageRank updated over m edge arrivals in a graph with n nodes when the algorithm stores R random walks per node and the PageRank teleport probability is . To prove this runn...

As the increasing of importance in search engines, Internet users change their behavior browsing the Internet little by little. In recent years, most part of search engines use link analysis algorithms to measure the importance of web pages. They employ the conventional flat web graph constructed by web pages and link relation of web pages to measure the relative importance of web pages. The mo...

The PageRank algorithm enables to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter α ∈]0, 1[. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of PageRank vector at its emergence when α → 1. The whole network...