Efficient algorithm to compute Markov transitional probabilities for a desired PageRank

On PageRank Algorithm and Markov Chain Reduction

The PageRank is used by search engines to reflect the popularity and importance of a page based on its reference ranking. Since the web changes very fast, the PageRank has to be regularly updated. Such updates is an challenging task due to the huge size of the World Wide Web. Consequently, the analysis of the PageRank has become a hot topic with vast literature ranging from the original paper b...

A Novel Approach to Feature Selection Using PageRank algorithm for Web Page Classification

In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analy...

Application of Markov Chain in the PageRank Algorithm

Link analysis algorithms for Web search engines determine the importance and relevance of Web pages. Among the link analysis algorithms, PageRank is the state of the art ranking mechanism that is used in Google search engine today. The PageRank algorithm is modeled as the behavior of a randomized Web surfer; this model can be seen as Markov chain to predict the behavior of a system that travels...

An Efficient Algorithm To Compute Maximum Entropy Densities

Efficient algorithm to compute mutually connected components in interdependent networks.

Mutually connected components (MCCs) play an important role as a measure of resilience in the study of interdependent networks. Despite their importance, an efficient algorithm to obtain the statistics of all MCCs during the removal of links has thus far been absent. Here, using a well-known fully dynamic graph algorithm, we propose an efficient algorithm to accomplish this task. We show that t...