The web link graph has a nested block structure: the vast majority of hyperlinks link pages on a host to other pages on the same host, and many of those that do not link pages within the same domain. We show how to exploit this structure to speed up the computation of PageRank by a 3-stage algorithm whereby (1) the local PageRanks of pages for each host are computed independently using the link...

The paper introduces a novel algorithm derived from the PageRank algorithm of Brin and Page. The PageRank algorithm interprets an hyperlink from page a to page b as being a positive vote from a to b. Starting from this interpretation, it attributes a rank to each page. However, it does not offer the possibility to take into account negative votes. The PageTrust algorithm includes negative links...

Link based analysis of web graphs has been extensively explored in many research projects. PageRank computation is one widely known approach which forms the basis of the Google search. PageRank assigns a global importance score to a web page based on the importance of other web pages pointing to it. PageRank is an iterative algorithm applying on a massively connected graph corresponding to seve...

In this work we consider the problem of maximizing the PageRank of a given target node in a graph by adding k new links. We consider the case that the new links must point to the given target node (backlinks). Previous work shows that this problem has no fully polynomial time approximation schemes unless P = NP . We present a polynomial time algorithm yielding a PageRank value within a constant...

In this work we consider the problem of maximizing the PageRank of a given target node in a graph by adding k new links. We consider the case that the new links must point to the given target node (backlinks). Previous work [7] shows that this problem has no fully polynomial time approximation schemes unless P = NP . We present a polynomial time algorithm yielding a PageRank value within a cons...

Ranking authors is vital for identifying a researcher’s impact and his standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The Author-Conf...

The PageRank equation computes the importance of pages in a web graph relative to a single random surfer with a constant teleportation coefficient. To be globally relevant, the teleportation coefficient should account for the influence of all users. Therefore, we correct the PageRank formulation by modeling the teleportation coefficient as a random variable distributed according to user behavio...

Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to add...

The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. PageRank computation includes repeated iterative sparse matrix-vector multiplications. Due to the enourmous size of the Web matrix to be multiplied, PageRank computations are usually carried out on parallel systems. Graph and hypergraph par...