PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
PageRank: Functional dependencies
ACM Transactions on Information Systems (TOIS)
Google PageRanking problem: The model and the analysis
Journal of Computational and Applied Mathematics
Regularization-based solution of the PageRank problem for large matrices
Automation and Remote Control
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PageRank is defined as the stationary state of a Markov chain depending on a damping factor α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α=0.85 by Brin and Page is still used. It is common belief that values of α closer to 1 give a "truer to the web" PageRank, but a small α accelerates convergence. Recently, however, it has been shown that when α=1 all pages in the core component are very likely to have rank 0 [1]. This behaviour makes it difficult to understand PageRank when α≈1, as it converges to a meaningless value for most pages. We propose a simple and natural modification to the standard preprocessing performed on the adjacency matrix of the graph, resulting in a ranking scheme we call TruRank. TruRank ranks the web with principles almost identical to PageRank, but it gives meaningful values also when α☰ 1.