Penalty Matrix-based PageRank Algorithm

Authors

  • B. Jaganathan Division of Mathematics, School of Advanced Sciences, VIT University, Chennai 600127
  • Kalyani Desikan Division of Mathematics, School of Advanced Sciences, VIT University, Chennai 600127

DOI:

https://doi.org/10.26713/jims.v9i3.813

Keywords:

Page rank, Eigen values, Eigen vector, Penalty matrix

Abstract

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.

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References

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Published

2017-10-31
CITATION

How to Cite

Jaganathan, B., & Desikan, K. (2017). Penalty Matrix-based PageRank Algorithm. Journal of Informatics and Mathematical Sciences, 9(3), 649–656. https://doi.org/10.26713/jims.v9i3.813

Issue

Vol. 9 No. 3 (2017)

Section

Research Articles

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