Proximity Prestige using Incremental Iteration in Page Rank Algorithm

Background/Objectives: Search engines such as Google uses the indexed results to respond to the user’s query. It also uses ranking algorithm namely Pagerank algorithm to rank the web pages. Ranking mechanism works offline and the value is static. Pagerank algorithm uses uniform probability distribution and Power iteration to rank the web. Though the method is simple and efficient, applying it to large scale is not effective since the computational cost is expensive and the convergence occurs at slower rate. Methods: The proposed work Proximity Prestige algorithm uses degree prestige along with proximity prestige and the calculation method used is Incremental Iteration. In Degree Prestige, a web page has more prestige if it has many in links. Proximity Prestige is defined by the closeness of other web pages that links to that page. It uses non-uniform transition probability for computing the rank vector. Findings: This work enhances the rank of a web page. From this work it is proven that when a page is more close to its home page and if it receives more in links, its rank value increases. Applications: It can be used by the search engines to compute the rank of a web page in accordance with the proximity and the incremental iteration method could be applied in the area where computational cost is expensive.