The pages and hyperlinks of the World-Wide Web may be viewed as nodes and arcs in a directed graph. This graph has about a billion nodes today, several billion links, and appears to grow exponentially with time. Known facts about macroscopic structure, diameter and in-degree and out-degree distributions of the graph are reviewed. The PageRank as another way of characterizing structure of the We...

This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0...

Large-scale network and graph analysis has received considerable attention recently. Graph mining techniques often involve an iterative algorithm, which can be implemented in a variety of ways. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using the...

The personalized PageRank algorithm is one of the most versatile tools for analysis networks. In spite its ubiquity, maintaining vectors when underlying network constantly evolves still a challenging task. To address this limitation, work proposes novel distributed to locally update graph topology changes. proposed based on use Chebyshev polynomials and equation that encompasses large family Pa...

We propose a popularity weighted ranking algorithm for academic digital libraries that uses the popularity factor of a publication venue overcoming the limitations of impact factors. We compare our method with the naive PageRank, citation counts and HITS algorithm, three popular measures currently used to rank papers beyond lexical similarity. The ranking results are evaluated by discounted cum...

Current search engines rely on centralized page ranking algorithms which compute page rank values as single (global) values for each Web page. Recent work on topic-sensitive PageRank [6] and personalized PageRank [8] has explored how to extend PageRank values with personalization aspects. To achieve personalization, these algorithms need specific input: [8] for example needs a set of personaliz...

Eigenvector based methods in general, and Google’s PageRank algorithm for rating web pages in particular, have become an important component of information retrieval on the Web. In this paper, we study the efficacy of, and countermeasures for, collusions designed to improve user rating in such systems. We define a metric, called the amplification factor, which captures the amount of PageRank-in...