Inside PageRank

By Monica Bianchini, Marco Gori, Franco Scarselli. TOIT, 2005

648 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/715c4e7acdf5bf848e6c988d9f116a65b63a0501

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Deeper Inside PageRank (2004, methodology): “These bounds are similar in spirit to the bounds of Ng et al. [94] and Bianchini et al. [19] presented in section 7.”
Image ranking based on user browsing behavior (2012, methodology): “Using the power method, the overall complexity of the algorithm is O(N log(1/ )), with N number of edges in the graph and a given degree of precision [1].”
Reinforcement Ranking (2013, methodology): “We first briefly review the formulation of Page Rank [6, 8, 12, 26].”
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian (2018, background): “Initially introduced as a ranking mechanism for hyperlinked webpages, PageRank has been extensively used and studied in other contexts [16, 59, 81].”
EigenRank: a ranking-oriented approach to collaborative filtering (2008, methodology): “In our model, since the transition probabilities p(j|i)’s are calculated using Equation 8, the entries of P are all non-negative, which could guarantee the existence and uniqueness of the stationary distribution π[1].”
Measuring user influence on Twitter: A survey (2015, methodology): “There are also fast iteration methods like the power method, that allows to compute the dominant eigenpair of a matrix [6].”
Web Spam Taxonomy (2005, background): “Recent analyses of the algorithm [2, 6] showed that the total PageRank score rtotal of a group of pages (at the extreme, a single page) depends on four factors: rtotal = rstatic + rin − rout − rsink, where rstatic is the score gained from the static score distribution (random jump); rin is the score”
Measuring the value of electronic word of mouth and its impact in consumer communities (2007, methodology): “According to Bianchini et al. (2005), the PageRank ( x p ) of page p is computed by taking into account the set of pages (pa[p]) pointing to p where d (cid:1) (0,1) is a proportioning factor and h q is the outdegree of q , the number of links coming out from page q .”
Virtual network embedding through topology-aware node ranking (2011, background): “, Algorithm 1) has been shown to yield any desired precision with a number of iterations proportional to max{1,−log } [6].”
A Survey of Solutions to the Sybil Attack (2006, methodology): “A Sybil attack is also used by companies that increase the Google PageRank rating of the pages of their customers [9], and”
Graph neural networks for ranking Web pages (2005, background): “The approach is quite efficient in practice, because even if the web is represented by a very large graph, the exponential convergence ensures that the solution can be computed in few tens of iterations [2, 3].”
The query-flow graph: model and applications (2008, methodology): “85, as it is customary in the PageRank literature [4], and used the `1-norm of the difference of two successive iterates to decide when to stop.”
A Survey of Eigenvector Methods for Web Information Retrieval (2005, background): “Several other ways of forcing irreducibility have been suggested and analyzed [40, 9, 59], but Google seems to favor the E = ev approach.”
An Uncertainty-Aware Approach for Exploratory Microblog Retrieval (2015, background): “The overall ranking score of a cluster rc is defined as the sum of the ranking scores of its items [4].”
Combating Web Spam with TrustRank (2004, background): “o = [1, 1, 1, 1, 0, 0, 0], t0 = [1, 12 , 1, 1 2 , 1 2 , 0, 1 2 ].”