The World Wide Web consists billions of web pages and hugs amount of information available within web pages. To retrieve required information from World Wide Web, search engines perform number of tasks based on their respective architecture. When a user refers a query to the search engine, it generally returns a large number of pages in response to user’s query. To support the users to navigate...

In the past few decades, The World Wide Web has expanded vigorously and so has the information accessible on The Internet. In today’s world, it would be impossible to navigate through this gigantic network without the help of a modern Web Search Engine to find the relevant information. Since introduction of PageRank, many variations of it have been proposed which mainly focus on link structure ...

As the use of web is increasing more day by day, the web users get easily lost in the web’s rich hyper structure. The main aim of the owner of the website is to give the relevant information according their needs to the users. We explained the Web mining is used to categorize users and pages by analyzing user’s behavior, the content of pages and then describe Web Structure mining. This paper in...

In this article we will present a graph partitioning algorithm which partitions a graph into two different types of components: the well-known ‘strongly connected components’ as well as another type of components we call ‘connected acyclic component’. We will give an algorithm based on Tarjan’s algorithm for finding strongly connected components used to find such a partitioning. We will also sh...

Computationally prioritizing disease genes by large-scale bio-experimental data can provide important insights into the underlying mechanism of complex diseases. Here, we explore the topology of the protein-protein interaction network and apply the PageRank algorithm to identify candidate genes relating to type 2 diabetes. Importantly, a novel idea is introduced to rank the disease genes in tis...

The PageRank algorithm is an important component for ranking Web pages in Google and other search engines. While many improvements for the original PageRank algorithm have been proposed, it is unclear which variations (and their combinations) provide the “best” ranked results. In this paper, we evaluate the ranking quality of the well-known variations of the original PageRank algorithm and thei...

We propose an improvement on a state-of-the-art keyphrase extraction algorithm, Topical PageRank (TPR), incorporating topical information from topic models. While the original algorithm requires a random walk for each topic in the topic model being used, ours is independent of the topic model, computing but a single PageRank for each text regardless of the amount of topics in the model. This in...

How does Google decide which web sites are important? It uses an ingenious algorithm that exploits the structure of the web and is resistant to hacking. Here, we describe this PageRank algorithm, illustrate it by example, and show how it can be interpreted as a Jacobi iteration and a teleporting random walk. We also ask the algorithm to rank the undergraduate mathematics classes offered at the ...

For computing PageRank problems, a Power–Arnoldi algorithm is presented by periodically knitting the power method together with thick restarted Arnoldi algorithm. In this paper, using extrapolation process based on trace (PET), variant of developed for accelerating computations. The new called Arnoldi-PET algorithm, whose implementation and convergence are analyzed. Numerical experiments severa...