Penalty Matrix-based PageRank Algorithm
B. Jaganathan, Kalyani Desikan · Journal of Informatics and Mathematical Sciences · 2017
In this paper we give a brief overview of the adjacency matrix based page rank algorithm and eigen vector based page rank that are used in the Google search engine. In this paper a new approach has been introduced by considering the web as a mixed graph rather than a simple graph. We propose an improved method for the computation of page rank on the basis of penalty assigned to web pages which are accessed through Advertisement links/pages. Consequently, we have applied the concept of column-stochastic Penalty Matrix to web page ranking. This approach does not involve any iterative technique. This method is based only on the concept of Eigen values and Eigen vectors of the Penalty matrix.