PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, ignoring higher-order structures captured by motifs, subgraphs consisting of a small number of nodes in complex networks. In this paper, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order s...

Personalized versions of PageRank have been proposed to rank the results of a search engine based on a user’s topic or query of interest. This paper introduces a methodology for personalizing PageRank vectors based on URL features such as Internet domains. Users specify interest profiles as binary feature vectors where a feature corresponds to a DNS tree node. Given a profile vector, a weighted...

Pagerank algorithm is a link analysis approach to evaluate the importance of web pages, and there are many techniques to improve the traditional Pagerank algorithm to prevent from the biases of link spamming in recent years. A key challenge for link analysis is to identify the relevance between the original page and the linked page. The importance scores of web pages should rely on the quality ...

PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer and thus it reflects the popularity of a Web page. Google computes the PageRank using the power iteration method which requires about one week of intensive computations. In the present work we propose and analyze Monte Carlo ty...

PageRank algorithm is one of the most commonly used algorithms that determine the global importance of web pages. Due to the size of web graph which contains billions of nodes, computing a PageRank vector is very computational intensive and it may takes any time between months to hours depending on the efficiency of the algorithm. This promoted many researchers to propose techniques to enhance ...

The Google search site (http://www.google.com) exploits the link structure of the Web to measure the relative importance of Web pages. The ranking method implemented in Google is called PageRank [3]. The sum of all PageRank values should be one. However, we notice that the sum becomes less than one in some cases. We present an improved PageRank algorithm that computes the PageRank values of the...

PageRank has numerous applications in information retrieval, reputation systems, machine learning, and graph partitioning. In this paper, we study PageRank in undirected random graphs with an expansion property. The Chung-Lu random graph is an example of such a graph. We show that in the limit, as the size of the graph goes to infinity, PageRank can be approximated by a mixture of the restart d...

In this study, the Finite-Element Model Updating (FEMU) technique is used to identify hyperelastic parameters from only one heterogeneous test. A residual considering measured and identified stretches as well global reaction force of specimen built. The originality paper investigate feasibility resolution minimisation problem using Inverse-PageRank-particle swarm optimisation (PSO) for identify...

This paper discusses efficient techniques for computing PageRank, a ranking metric for hypertext documents. We show that PageRank can be computed for very large subgraphs of the web (up to hundreds of millions of nodes) on machines with limited main memory. Running-time measurements on various memory configurations are presented for PageRank computation over the 24-million-page Stanford WebBase...