J. Hopcroft and D. Sheldon originally introduced the PageRank game to investigate the self-interested behavior of web authors who want to boost their PageRank by using game theoretical approaches. The PageRank game is a multiplayer game where players are the nodes in a directed web graph and they place their outlinks to maximize their PageRank value. They give best response strategies for each ...

The PageRank algorithm is an iterative algorithm used in the Google search engine to improve the results of requests by taking into account the link structure of the web. More interesting and intelligent surfer model combining the link and content information in PageRank have been proposed in the literature. The main disadvantage of those models is that the combination of single word PageRank t...

Ranking systems are central to many internet applications including, notably, Google’s PageRank algorithm for ranking web pages. Ranking systems are a special case of a social choice problem in which the set of agents and the set of outcomes coincide. In this paper we consider PageRank as a particular ranking system and we present two axiomatizations that allow PageRank to be studied from a soc...

We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web page...

The PageRank is used by search engines to reflect the popularity and importance of a page based on its reference ranking. Since the web changes very fast, the PageRank has to be regularly updated. Such updates is an challenging task due to the huge size of the World Wide Web. Consequently, the analysis of the PageRank has become a hot topic with vast literature ranging from the original paper b...

This paper describes and compares two trust metrics, Advogato and PageRank. It describes a novel attack against Advogato and gives an attackresistance proof for PageRank.

Sep Kamvar’s monograph presents work from his 2004 thesis on how numerical algorithms are a critical component in search systems. Kamvar focuses on the new information networks arising in the late 1990s and early 2000s. When originally published, these results were groundbreaking and resulted in an NSF press release and even a Slashdot listing. 2 The book is divided into two parts, one for each...

In this paper, we consider the problem of calculating fast and accurate approximations to the personalized PageRank score ([8, 16]) of a webpage. We focus on techniques to improve speed by limiting the amount of webgraph data we need to access. PageRank scores are mainly used for ranking purposes, and generally only the scores exceeding a given threshold are relevant. In practice, and relative ...

PageRank has been widely used to measure the importance of web pages based on their interconnections in the web graph. Mathematically speaking, PageRank can be explained using a Markov random walk model, in which only the direct outlinks of a page contribute to its transition probability. In this paper, we propose improving the PageRank algorithm by looking N-step ahead when constructing the tr...

Deciding which kind of visit accumulates high-quality pages more quickly is one of the most often debated issue in the design of web crawlers. It is known that breadth-first visits work well, as they tend to discover pages with high PageRank early on in the crawl. Indeed, this visit order is much better than depth first, which is in turn even worse than a random visit; nevertheless, breadth-fir...