SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
Generalizing PageRank: damping functions for link-based ranking algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking the random surfer: empirically measured teleportation parameters in PageRank
Proceedings of the 19th international conference on World wide web
Journal of Computational and Applied Mathematics
Dynamic pagerank using evolving teleportation
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
NCDawareRank: a novel ranking method that exploits the decomposable structure of the web
Proceedings of the sixth ACM international conference on Web search and data mining
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PageRank is defined as the stationary state of a Markov chain obtained by perturbing the transition matrix of a web graph with a damping factor α that spreads part of the rank. The choice of α is eminently empirical, but most applications use α = 0.85; nonetheless, the selection of α is critical, and some believe that link farms may use this choice adversarially. Recent results [1] prove that the PageRank of a page is a rational function of α, and that this function can be approximated quite efficiently: this fact can be used to define a new form of ranking, TotalRank, that averages PageRanks over all possible α's. We show how this rank can be computed efficiently, and provide some preliminary experimental results on its quality and comparisons with PageRank.