Motivated by the problem of detecting link-spam, we consider the following graph-theoretic primitive: Given a webgraph G, a vertex v in G, and a parameter δ ∈ (0, 1), compute the set of all vertices that contribute to v at least a δ fraction of v’s PageRank. We call this set the δ-contributing set of v. To this end, we define the contribution vector of v to be the vector whose entries measure t...

Recent works aimed to understand how identify “milestone” scientific papers of great significance from large-scale citation networks. To this end, previous results found that global ranking metrics take into account the whole network structure (such as Google’s PageRank) outperform local such count. Here, we show by leveraging recursive equation defines PageRank algorithm, can propose a family ...

A local graph partitioning algorithm finds a cut near a specified starting vertex, with a running time that depends largely on the size of the small side of the cut, rather than the size of the input graph. In this paper, we present a local partitioning algorithm using a variation of PageRank with a specified starting distribution. We derive a mixing result for PageRank vectors similar to that ...

Anti-spamming has become one of the most important challenges to web search engines and attracted increasing attention in both industry and academia recently. Since most search engines now use link-based ranking algorithms, link-based spamming has become a major threaten. In this paper, we show that the popular link-based ranking algorithm PageRank, while being successfully used in the Google s...

Web search engines use link-based reputation systems (e.g. PageRank) to measure the importance of web pages, giving rise to the strategic manipulations of hyperlinks by spammers and others to boost their web pages’ reputation scores. Hopcroft and Sheldon [10] study this phenomenon by proposing a network formation game in which nodes strategically select their outgoing links in order to maximize...

Searching the World Wide Web is an NP complete problem with sparse hyperlink matrices. Thus searching the significant search results is a challenge. Google’s PageRank attempted to solve this problem using computing of principle Eigenvalues termed as PageRank vector. After this, a number of techniques were developed to speed up the convergence patterns of pages in the PageRank algorithm. This is...

We present a fast two-stage algorithm for computing the PageRank vector [16]. The algorithm exploits the following observation: the homogeneous discrete-time Markov chain associated with PageRank is lumpable, with the lumpable subset of nodes being the dangling nodes [13]. Time to convergence is only a fraction of what’s required for the standard algorithm employed by Google [16]. On data of 45...