PageRank beyond the Web

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...

Google’s PageRank and Beyond: The Science of Search Engine Rankings This page intentionally left blank Google’s PageRank and Beyond: The Science of Search Engine Rankings

Towards Supporting Exploratory Search over the Arabic Web Content: The Case of ArabXplore

Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from exte...

Google's PageRank and beyond - the science of search engine rankings

Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book...

Using PageRank to Characterize Web Structure

Recent work on modeling the Web graph has dwelt on capturing the degree distributions observed on the Web. Pointing out that this represents a heavy reliance on “local” properties of the Web graph, we study the distribution of PageRank values (used in the Google search engine) on the Web. This distribution is of independent interest in optimizing search indices and storage. We show that PageRan...