PageRank is one of the most popular link analysis algorithms that have shown their effectiveness in web search. However, PageRank only consider hyperlink information. In this paper, we propose several novel ranking algorithms, which make use of both hyperlink and site structure information to measure the importance of each web page. Specifically, two kinds of methodologies are adopted to refine...

Semi-supervised and unsupervised machine learning methods often rely on graphs to model data, prompting research how theoretical properties of operators are leveraged in problems. While most the existing literature focuses undirected graphs, directed very important practice, giving models for physical, biological or transportation networks, among many other applications. In this paper, we propo...

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...

We propose FrogWild, a novel algorithm for fast approxi-mation of high PageRank vertices, geared towards reducingnetwork costs of running traditional PageRank algorithms.Our algorithm can be seen as a quantized version of poweriteration that performs multiple parallel random walks overa directed graph. One important innovation is that we in-troduce a modification to the ...

In a network, identifying all vertices whose PageRank is more than a given threshold value ∆ is a basic problem that has arisen in Web and social network analyses. In this paper, we develop a nearly optimal, sublinear time, randomized algorithm for a close variant of this problem. When given a directed network G = (V,E), a threshold value ∆, and a positive constant c > 3, with probability 1 − o...

Evaluating the impact of scholarly papers plays an important role for addressing recruitment decision, funding allocation and promotion, etc. Yet little is known how actual geographic distance influences the impact of scholarly papers. In this paper, we leverage the law of geographic distance and citations between different institutions to weight quantum Pagerank algorithm for objectively measu...

Started in 1998, the search engine Google estimates page importance using several parameters. PageRank is one of those. Precisely, PageRank is a distribution of probability on the Web pages that depends on the Web graph. Our purpose is to show that the PageRank can be decomposed into two terms, internal and external PageRank. These two PageRanks allow a better comprehension of the PageRank sign...