Gotowe bibliografie tematyczne / PageRank method

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Gotowa bibliografia na temat „PageRank method”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "PageRank method"

Bu, Yi-Ming, and Ting-Zhu Huang. "An Adaptive Reordered Method for Computing PageRank." Journal of Applied Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/507915.

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We propose an adaptive reordered method to deal with the PageRank problem. It has been shown that one can reorder the hyperlink matrix of PageRank problem to calculate a reduced system and get the full PageRank vector through forward substitutions. This method can provide a speedup for calculating the PageRank vector. We observe that in the existing reordered method, the cost of the recursively reordering procedure could offset the computational reduction brought by minimizing the dimension of linear system. With this observation, we introduce an adaptive reordered method to accelerate the tot

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Shen, Zhao-Li, Yu-Tong Liu, Bruno Carpentieri, Chun Wen, and Jian-Jun Wang. "Recursive reordering and elimination method for efficient computation of PageRank problems." AIMS Mathematics 8, no. 10 (2023): 25104–30. http://dx.doi.org/10.3934/math.20231282.

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<abstract>The PageRank model is widely utilized for analyzing a variety of scientific issues beyond its original application in modeling web search engines. In recent years, considerable research effort has focused on developing high-performance iterative methods to solve this model, particularly when the dimension is exceedingly large. However, due to the ever-increasing extent and size of data networks in various applications, the computational requirements of the PageRank model continue to grow. This has led to the development of new techniques that aim to reduce the computat

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GLEICH, D. F., and K. KLOSTER. "Seeded PageRank solution paths." European Journal of Applied Mathematics 27, no. 6 (2016): 812–45. http://dx.doi.org/10.1017/s0956792516000280.

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We study the behaviour of network diffusions based on the PageRank random walk from a set of seed nodes. These diffusions are known to reveal small, localized clusters (or communities), and also large macro-scale clusters by varying a parameter that has a dual-interpretation as an accuracy bound and as a regularization level. We propose a new method that quickly approximates the result of the diffusion for all values of this parameter. Our method efficiently generates an approximatesolution pathorregularization pathassociated with a PageRank diffusion, and it reveals cluster structures at mult

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Pu, Bing-Yuan, Ting-Zhu Huang, Chun Wen, and Yi-Qin Lin. "The Extrapolation-Accelerated Multilevel Aggregation Method in PageRank Computation." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/525313.

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An accelerated multilevel aggregation method is presented for calculating the stationary probability vector of an irreducible stochastic matrix in PageRank computation, where the vector extrapolation method is its accelerator. We show how to periodically combine the extrapolation method together with the multilevel aggregation method on the finest level for speeding up the PageRank computation. Detailed numerical results are given to illustrate the behavior of this method, and comparisons with the typical methods are also made.

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Shen, Zhao-Li, Hao Yang, Bruno Carpentieri, Xian-Ming Gu, and Chun Wen. "A Preconditioned Variant of the Refined Arnoldi Method for Computing PageRank Eigenvectors." Symmetry 13, no. 8 (2021): 1327. http://dx.doi.org/10.3390/sym13081327.

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The PageRank model computes the stationary distribution of a Markov random walk on the linking structure of a network, and it uses the values within to represent the importance or centrality of each node. This model is first proposed by Google for ranking web pages, then it is widely applied as a centrality measure for networks arising in various fields such as in chemistry, bioinformatics, neuroscience and social networks. For example, it can measure the node centralities of the gene-gene annotation network to evaluate the relevance of each gene with a certain disease. The networks in some fi

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Vlasyuk, Vladislav, Oleg Galchonkov, and Alexander Nevrev. "An algebraic method for calculating PageRank." Eastern-European Journal of Enterprise Technologies 3, no. 2 (93) (2018): 6–12. http://dx.doi.org/10.15587/1729-4061.2018.131275.

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Yao, Yuhang, Xiao Zeng, Tianyue Cao, Luoyi Fu, and Xinbing Wang. "APRP: An Anonymous Propagation Method in Bitcoin Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10073–74. http://dx.doi.org/10.1609/aaai.v33i01.330110073.

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Due to little attention given to anonymous protection against eavesdropping attacks in Bitcoin network, this paper initiatively proposes a solution to Bitcoin anonymization based on network structure. We first present a general adversarial network model for formulizing deanonymization attack, then present a novel propagation method APRP(Adaptive PageRank Propagation) that adopts PageRank as propagation delay factor and constantly adjusts PR-value of nodes to adapt to network dynamics. Experiments on both simulated and real Bitcoin networks confirm the superiority of APRP in terms of 20-50% per

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Feng, Yuehua, Yongxin Dong, and Jianxin You. "A Note on a Minimal Irreducible Adjustment Pagerank." Symmetry 14, no. 8 (2022): 1640. http://dx.doi.org/10.3390/sym14081640.

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The stochastic modification and irreducible modification in PageRank produce large web link changes correspondingly. To get a minimal irreducible web link adjustment, a PageRank model of minimal irreducible adjustment and its lumping method are discussed by Li, Chen, and Song. In this paper, we provide alternative proofs for the minimal irreducible PageRank by a new type of similarity transformation matrices. To further provide theorems and fast algorithms on a reduced matrix, an 4×4 block matrix partition case of the minimal irreducible PageRank model is utilized and analyzed. For some real a

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Zhang, He Ping, Ya Ping Zhao, and Ya Li Zhao. "Research on PageRank Algorithm to Index Pages." Applied Mechanics and Materials 198-199 (September 2012): 1469–74. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1469.

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PageRank algorithm is a vital method to determine the importance of pages. Useful as it is, the algorithm has many disadvantages. Therefore, we arrive at the conclusion that it’s not rational to calculate the importance degree of pages simply by links between them. Considering the timeliness problem of PageRank algorithm, we provide the time penalty factor W(n) to weigh the effects of update time on page ranking. After adding the time penalty factor to the original PageRank algorithm, we come up with the refined PageRank algorithm. Our algorithm is superior compared with the original one and m

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Matthews, Nicole R., Andrew McClain, Chase M. L. Smith, and Adam G. Tennant. "Application of PageRank Algorithm to Division I NCAA men’s basketball as bracket formation and outcome predictive utility." Journal of Sports Analytics 7, no. 1 (2021): 1–9. http://dx.doi.org/10.3233/jsa-200425.

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This article examines the use of the PageRank algorithm to rank the teams and predict team performance in the tournament. This method has the potential to be utilized as an alternative method to choose tournament participants, as opposed to the traditional ranking and seeding process currently employed by the NCAA. PageRank allows for the consideration of all games played during the regular season (average of 5832 games per season) and for customizable performance weights in the prediction. The PageRank algorithm is a viable tool in predicting tournament outcomes due to depth and extensiveness

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Rozprawy doktorskie na temat "PageRank method"

Wills, Rebecca Smith. "When Rank Trumps Precision: Using the Power Method to Compute Google?s PageRank." NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-06122007-173712/.

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The PageRank algorithm, developed by Google founders Larry Page and Sergey Brin, assigns ranking scores to webpages that reflect their relative importance. These scores are based primarily on the link structure of the Web graph and correspond to elements of a dominant left eigenvector, called the PageRank vector, of the stochastic Google matrix. When the starting vector is a probability vector, the iterates of the power method applied to the Google matrix converge to the PageRank vector. Determining when to stop the iterations requires deciding when an iterate vector is good enough. Existing t

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Vasireddy, Jhansi Lakshmi. "Applications of Linear Algebra to Information Retrieval." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/71.

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Some of the theory of nonnegative matrices is first presented. The Perron-Frobenius theorem is highlighted. Some of the important linear algebraic methods of information retrieval are surveyed. Latent Semantic Indexing (LSI), which uses the singular value de-composition is discussed. The Hyper-Text Induced Topic Search (HITS) algorithm is next considered; here the power method for finding dominant eigenvectors is employed. Through the use of a theorem by Sinkohrn and Knopp, a modified HITS method is developed. Lastly, the PageRank algorithm is discussed. Numerical examples and MATLAB programs

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Nemirovsky, Danil. "Monte Carlo methods and Markov chain based approaches for PageRank computation." Nice, 2010. http://www.theses.fr/2010NICE4018.

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Caverlee, James. "Tamper-Resilient Methods for Web-Based Open Systems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16286.

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The Web and Web-based open systems are characterized by their massive amount of data and services for leveraging this data. These systems are noted for their open and unregulated nature, self-supervision, and high degree of dynamism, which are key features in supporting a rich set of opportunities for information sharing, discovery, and commerce. But these open and self-managing features also carry risks and raise growing concerns over the security and privacy of these systems, including issues like spam, denial-of-service, and impersonated digital identities. Our focus in this thesis is on t

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Fender, Alexandre. "Solutions parallèles pour les grands problèmes de valeurs propres issus de l'analyse de graphe." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV069/document.

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Les graphes, ou réseaux, sont des structures mathématiques représentant des relations entre des éléments. Ces systèmes peuvent être analysés dans le but d’extraire des informations sur la structure globale ou sur des composants individuels. L'analyse de graphe conduit souvent à des problèmes hautement complexes à résoudre. À grande échelle, le coût de calcul de la solution exacte est prohibitif. Heureusement, il est possible d’utiliser des méthodes d’approximations itératives pour parvenir à des estimations précises. Lesméthodes historiques adaptées à un petit nombre de variables ne conviennen

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Kandiah, Vivek. "Application of the Google matrix methods for characterization of directed networks." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2434/.

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La théorie des réseaux complexes est un domaine récent et important de la recherche qui consiste étudier divers systèmes naturels ou artificiels d'un point de vue des graphes en considérant une collection d'objets interdépendants. Parmi les différents aspects de la théorie des réseaux complexes, cette thèse se concentre sur l'analyse des propriétés structurelles des réseaux dirigés. L'outil principal utilisé dans ce travail est la méthode de la matrice Google qui est une méthode dérivée de la théorie des chaînes de Markov. La construction de cette matrice et son lien avec les chaînes de Markov

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Liu, Chia-Sheng, and 劉家升. "DRank+: A Directory based PageRank Prediction Method for Fast PageRank Convergence." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/73139679471982609346.

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碩士 國立成功大學 資訊工程學系碩博士班 95 As the increasing of importance in search engines, Internet users change their behavior browsing the Internet little by little. In recent years, most part of search engines use link analysis algorithms to measure the importance of web pages. They employ the conventional flat web graph constructed by web pages and link relation of web pages to measure the relative importance of web pages. The most famous link analysis algorithm is PageRank algorithm. However, previous researches in recent years have found that there exists an inherent bias against newly creat

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Wills, Rebecca S. "When rank trumps precision using the power method to compute Google₂s pagerank /." 2006. http://www.lib.ncsu.edu/theses/available/etd-06122007-173712/unrestricted/etd.pdf.

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Części książek na temat "PageRank method"

Yin, Guo-Jian, and Jun-Feng Yin. "On Arnoldi Method Accelerating PageRank Computations." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16515-3_47.

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Sakakura, Yuta, Yuto Yamaguchi, Toshiyuki Amagasa, and Hiroyuki Kitagawa. "An Improved Method for Efficient PageRank Estimation." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10085-2_19.

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Butenko, Sergiy, Andrew Johnson, Erick Moreno-Centeno, and Justin Yates. "A PageRank-Based Method for College Football Recruiting Rankings." In Springer Optimization and Its Applications. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76047-1_12.

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Shao, Bo, Siyan Lai, Bo Yang, Ying Xu, and Xiaola Lin. "A Load Balancing Strategy for Monte Carlo Method in PageRank Problem." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6442-5_56.

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Hamed, Ahmed Abdeen, and Asim Zia. "Mining Climate Change Awareness on Twitter: A PageRank Network Analysis Method." In Computational Science and Its Applications -- ICCSA 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21404-7_2.

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Li, Qiuhong, Wei Wang, Peng Wang, et al. "Combination of In-Memory Graph Computation with MapReduce: A Subgraph-Centric Method of PageRank." In Web-Age Information Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38562-9_18.

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Carmona, Pablo, Juan Luis Castro, Jesús Lozano, and José Ignacio Suárez. "A PageRank-Based Method to Extract Fuzzy Expressions as Features in Supervised Classification Problems." In Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00374-6_15.

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Li, Hang. "The PageRank Algorithm." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_21.

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Waltman, Ludo, and Erjia Yan. "PageRank-Related Methods for Analyzing Citation Networks." In Measuring Scholarly Impact. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10377-8_4.

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Cozzo, Daniele, Nigel P. Smart, and Younes Talibi Alaoui. "Secure Fast Evaluation of Iterative Methods: With an Application to Secure PageRank." In Topics in Cryptology – CT-RSA 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75539-3_1.

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Streszczenia konferencji na temat "PageRank method"

"DRANK+: A DIRECTORY BASED PAGERANK PREDICTION METHOD FOR FAST PAGERANK CONVERGENCE." In 4th International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001521701750180.

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Kim, Kyung Soo, and Yong Suk Choi. "Incremental iteration method for fast PageRank computation." In IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication. ACM, 2015. http://dx.doi.org/10.1145/2701126.2701165.

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Degadwala, Sheshang, Dhairya Vyas, Khush Nilaykumar Patel, Mukesh Soni, Pavitar Parkash Singh, and Ramya Maranan. "Optimizing Hindi Paragraph Summarization through PageRank Method." In 2023 2nd International Conference on Edge Computing and Applications (ICECAA). IEEE, 2023. http://dx.doi.org/10.1109/icecaa58104.2023.10212107.

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Mendes, I. R., and P. B. Vasconcelos. "Lumping Method with Acceleration for the PageRank Computation." In 2014 14th International Conference on Computational Science and Its Applications (ICCSA). IEEE, 2014. http://dx.doi.org/10.1109/iccsa.2014.50.

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Du, Yanan, and Yueming Lu. "A Weakness Relevance Evaluation Method Based on PageRank." In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). IEEE, 2019. http://dx.doi.org/10.1109/dsc.2019.00070.

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Huang, Zhan, Shun Long, Yuying Jiang, and Qian Chen. "An Outlier Detection Method Based on PageRank Algorithm." In 2016 12th International Conference on Computational Intelligence and Security (CIS). IEEE, 2016. http://dx.doi.org/10.1109/cis.2016.0081.

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Huang, Wei, and Bin Li. "An improved method for the computation of PageRank." In 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC). IEEE, 2011. http://dx.doi.org/10.1109/mec.2011.6025926.

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Zhu*, Huiyu, and Jie Zhang. "The PageRank method for automatic detection of microseismic events." In SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, 2014. http://dx.doi.org/10.1190/segam2014-1231.1.

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Nayak, Snigdhashree, Mahesh Arunprasath, and Nachiketa Mishra. "Two Stage Chebyshev Semi-Iterative Method for PageRank Problem." In 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2022. http://dx.doi.org/10.1109/iatmsi56455.2022.10119387.

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Qian, Quan, Jianyu Li, Jing Cai, Rui Zhang, and Mingjun Xin. "An Anomaly Intrusion Detection Method Based on PageRank Algorithm." In 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom). IEEE, 2013. http://dx.doi.org/10.1109/greencom-ithings-cpscom.2013.421.

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Raporty organizacyjne na temat "PageRank method"

Bain, Rachel, David Young, Marin Kress, Katherine Chambers, and Brandan Scully. US port connectivity and ramifications for maintenance of South Atlantic Division ports. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46385.

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This study utilized automatic identification system (AIS) data to quantify vessel traffic patterns within a predominantly US port network from 1 January 2009 to 31 December 2020, with the methods validated using independent data sets collected between 1 January 2015 and 31 December 2019. The analysis focused on South Atlantic Division (SAD) ports. AIS-derived data characterized individual ports’ traffic and port-to-port connectivity for the network. With foreign vessel entrances and clearances (E&C) data, the AIS-reported vessel characteristics enabled calculation of ships’ physical volume

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Langlais, Pierre-Carl. Bibliometrics. Comité pour la science ouverte, 2023. https://doi.org/10.52949/73.

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Bibliometrics is deeply transformed by open science. The unprecedented availability of bibliographic metadata, full text search and additional use metrics creates new opportunities for quantitative studies of scientific corpus. It also challenges the historical focus of bibliometrics on citation data from a few selected journals. New approaches aim to expand its scope to different uses and different publics, especially in regard to the social impact of research. As a field, bibliometrics has been strongly influenced by the first commercial infrastructure for academic publications. In the 1960s

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