Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
A Theoretical Analysis of Google's PageRank
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Proceedings of the 13th international conference on World Wide Web
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
UbiCrawler: a scalable fully distributed web crawler
Software—Practice & Experience
TotalRank: ranking without damping
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
TruRank: taking PageRank to the limit
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Hyperlink analysis on the world wide web
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
Beyond PageRank: machine learning for static ranking
Proceedings of the 15th international conference on World Wide Web
Building implicit links from content for forum search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Link analysis using time series of web graphs
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Ranking web sites with real user traffic
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
DirichletRank: Solving the zero-one gap problem of PageRank
ACM Transactions on Information Systems (TOIS)
BrowseRank: letting web users vote for page importance
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Choose the Damping, Choose the Ranking?
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
PageRank: Functional dependencies
ACM Transactions on Information Systems (TOIS)
Proceedings of the 18th ACM conference on Information and knowledge management
Nonlinear static-rank computation
Proceedings of the 18th ACM conference on Information and knowledge management
PageRank for ranking authors in co-citation networks
Journal of the American Society for Information Science and Technology
Eigenvectors of directed graphs and importance scores: dominance, T-Rank, and sink remedies
Data Mining and Knowledge Discovery
A framework to compute page importance based on user behaviors
Information Retrieval
Choose the damping, choose the ranking?
Journal of Discrete Algorithms
ECIR'07 Proceedings of the 29th European conference on IR research
Tracking the random surfer: empirically measured teleportation parameters in PageRank
Proceedings of the 19th international conference on World wide web
Distribution of PageRank mass among principle components of the web
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Optimizing web structures using web mining techniques
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
The general extrapolation formula for acceleration PageRank computations
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Network as a computer: ranking paths to find flows
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
Google PageRanking problem: The model and the analysis
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Discovering author impact: A PageRank perspective
Information Processing and Management: an International Journal
An Inner-Outer Iteration for Computing PageRank
SIAM Journal on Scientific Computing
Page importance computation based on Markov processes
Information Retrieval
NewPR-Combining TFIDF with pagerank
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
So who won?: dynamic max discovery with the crowd
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Top-k Similar Graph Matching Using TraM in Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Applying reinforcement learning for web pages ranking algorithms
Applied Soft Computing
Incorporating the surfing behavior of web users into pagerank
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with 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. Recently, however, the behaviour of PageRank with respect to changes in α was discovered to be useful in link-spam detection[21]. Moreover, an analytical justification of the value chosen for α is still missing. In this paper, we give the first mathematical analysis of PageRank when α changes. In particular, we show that, contrarily to popular belief, for real-world graphs values of α close to 1 do not give a more meaningful ranking. Then, we give closed-form formulae for PageRank derivatives of any order, and an extension of the Power Method that approximates them with convergence O (tk αt) for the k-th derivative. Finally, we show a tight connection between iterated computation and analytical behaviour by proving that the k-th iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k. The latter result paves the way towards the application of analytical methods to the study of PageRank.