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Knowledge diffusion trajectories of pagerank: A main path analysis

JOURNAL OF INFORMATION SCIENCE 2024年第1期50卷 273-287页

作者： Yu, Dejian Yan, Zhaoping Nanjing Audit Univ Business Sch Nanjing Peoples R China Nanjing Audit Univ Business Sch Nanjing 211815 Jiangsu Peoples R China

In the era of the Internet and big data, the pagerank (PR) algorithm is a constantly evolving research field. However, there is no systematic research to explore the overall development trend of the PR domain. This article evaluates 1446 articles related to the PR algorithm and provides a thorough understanding of the PR field through the main path analysis (MPA). Through two basic main paths, a number of papers that play a leading role have been identified, which outline the backbone of the PR domain. Based on the analysis of multiple main paths, four main subareas have been investigated. There are accelerating the computation of PR, comprehensive applications of PR, researches on academic impact assessment and age preference in network evolution. Finally, this article discusses the research findings and the future directions of the PR field. It is the first attempt to identify the development trend of the PR domain through MPA, thus providing an insight into the knowledge evolution of the PR field over the past two decades.

关键词： Academic impact assessment knowledge diffusion main path analysis pagerank algorithm

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pagerank computation via a distributed randomized approach with lossy communication

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SYSTEMS & CONTROL LETTERS 2012年第12期61卷 1221-1228页

作者： Ishii, Hideaki Tempo, Roberto Bai, Er-Wei Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku Yokohama Kanagawa 2268502 Japan Politecn Torino CNR IEIIT I-10129 Turin Italy Univ Iowa Dept Elect & Comp Engn Seamans Ctr Engn Arts & Sci 4316 Iowa City IA 52242 USA Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

For ranking web pages in search results, Google employs the so-called pagerank algorithm, which provides a measure of importance to each page based on the web structure. Recently, we have developed a distributed randomized approach for this algorithm, where pages compute their own pagerank by communicating over selected links. In this paper, the focus is on the effects of unreliability in communication channels. Specifically, we consider random data losses modeled as a Markov chain and introduce a generalized version of the distributed scheme. Its convergence properties and the error in the approximated pagerank are analyzed. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Search engines Stochastic matrices

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A novel method for expert finding in online communities based on concept map and pagerank

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2015年第1期5卷 1页

作者： Rafiei, Majid Kardan, Ahmad A. Amirkabir Univ Technol Dept Comp Engn & IT Adv E Learning Lab Tehran Iran Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran

An online community is a virtual community where people can express their opinions and their knowledge freely. There are a great deal of information in online communities, however there is no way to determine its authenticity. Thus the knowledge which has been shared in online communities is not reliable. By determining expertise level of users and finding experts in online communities the accuracy of posted comments can be evaluated. In this study, a hybrid method for expert finding in online communities is presented which is based on content analysis and social network analysis. The content analysis is based on concept map and the social network analysis is based on pagerank algorithm. To evaluate the proposed method java online community was selected and then correlation between our results and scores prepared by java online community was calculated. Based on obtained results Spearman correlation for 11 subcategories of java online community using this method is 0.904, which is highly an acceptable value.

关键词： Expert finding Online community Concept map Dijkstra's algorithm Knowledge sharing pagerank algorithm

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Authority rankings from hits, pagerank, and salsa: Existence, uniqueness, and effect of initialization

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2006年第4期27卷 1181-1201页

作者： Farahat, A Lofaro, T Miller, JC Rae, G Ward, LA PARC Palo Alto CA 94304 USA Gustavus Adolphus Coll Dept Math & Comp Sci St Peter MN 56082 USA Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England Harvey Mudd Coll Dept Math Claremont CA 91711 USA

algorithms such as Kleinberg's HITS algorithm, the pagerank algorithm of Brin and Page, and the SALSA algorithm of Lempel and Moran use the link structure of a network of web pages to assign weights to each page in the network. The weights can then be used to rank the pages as authoritative sources. These algorithms share a common underpinning;they find a dominant eigenvector of a nonnegative matrix that describes the link structure of the given network and use the entries of this eigenvector as the page weights. We use this commonality to give a unified treatment, proving the existence of the required eigenvector for the pagerank, HITS, and SALSA algorithms, the uniqueness of the pagerank eigenvector, and the convergence of the algorithms to these eigenvectors. However, we show that the HITS and SALSA eigenvectors need not be unique. We examine how the initialization of the algorithms affects the final weightings produced. We give examples of networks that lead the HITS and SALSA algorithms to return nonunique or nonintuitive rankings. We characterize all such networks in terms of the connectivity of the related HITS authority graph. We propose a modi. cation, Exponentiated Input to HITS, to the adjacency matrix input to the HITS algorithm. We prove that Exponentiated Input to HITS returns a unique ranking, provided that the network is weakly connected. Our examples also show that SALSA can give inconsistent hub and authority weights, due to nonuniqueness. We also mention a small modi. cation to the SALSA initialization which makes the hub and authority weights consistent.

关键词： link analysis web search HITS algorithm Kleinberg's algorithm pagerank algorithm SALSA algorithm hubs authorities networks

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Diffpagerank: an efficient differential pagerank approach in MapReduce

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JOURNAL OF SUPERCOMPUTING 2021年第1期77卷 188-211页

作者： Abadeh, Maryam Nooraei Mirzaie, Mansooreh Islamic Azad Univ Abadan Branch Dept Comp Engn Abadan Iran Golpayegan Univ Technol Fac Elect & Comp Engn Golpayegan Iran

Unstructured big data processing requires efficient computational styles to rapidly analyze continuously changing data. Incremental processing is a promising technique to update search results without re-computing the whole process. One of the most important mining algorithms is pagerank which necessitates incremental processing and iterative computations to keep the mining results up-to-date. In this paper, a light and efficient pagerank algorithm is proposed using differential dataflow in the MapReduce programming style named Diffpagerank. The innovation of the proposed approach is investigated from two points of view: (1) the updating state is dependent on a partial order of changes in Diffpagerank computing, while the changes are applied based on complete order sequences in the standard incremental computing;and (2) in Diffpagerank, a set of updates rebuilds each version, while in the incremental systems, every single update continually bring up-to-date the current version state. Diffpagerank is compared with two other implementations of pagerank including standard and incremental pagerank in MapReduce in terms of processing time, CPU utilization and update speed of data mining results. The results show that if the changes on the input data are large, the efficiency of the differential method will considerably increase in terms of time than the standard and incremental pagerank approaches up to 61% and 86%, respectively. Diffpagerank is also compared with two state-of-the-art incremental graph processing benchmarks on Orkut and Twitter datasets in terms of execution time and the number of updates. The experimental comparisons on benchmark datasets show that our method reduces the number of updates and total execution time by using the differential MapReduce approach.

关键词： pagerank algorithm Big data MapReduce Differential dataflow Incremental processing

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Distributed Randomized algorithms for the pagerank Computation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2010年第9期55卷 1987-2002页

作者： Ishii, Hideaki Tempo, Roberto Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Yokohama Kanagawa 2268502 Japan Politecn Torino IEIIT CNR I-10129 Turin Italy

In the search engine of Google, the pagerank algorithm plays a crucial role in ranking the search results. The algorithm quantifies the importance of each web page based on the link structure of the web. We first provide an overview of the original problem setup. Then, we propose several distributed randomized schemes for the computation of the pagerank, where the pages can locally update their values by communicating to those connected by links. The main objective of the paper is to show that these schemes asymptotically converge in the mean-square sense to the true pagerank values. A detailed discussion on the close relations to the multi-agent consensus problems is also given.

关键词： Distributed computation multi-agent consensus pagerank algorithm randomization search engines stochastic matrices

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A Novel pagerank-Based Fault Handling Strategy for Workflow Scheduling in Cloud Data Centers

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INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2021年第4期18卷 1-26页

作者： Xie, Fei Yan, Jun Shen, Jun Univ Wollongong Fac Engn & Informat Sci Sch Comp & Informat Technol Wollongong NSW Australia Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

Unexpected faults result in unscheduled cloud outage, which negatively affects the completion of workflow tasks in the cloud. This paper presents a novel pagerank-based fault handling strategy to rescue workflow tasks at the faulty data center. The proposed approach uses a holistic view and considers the task attributes, the timeline scenario, and the overall cloud performance. A priority assignment system is developed based on the modified pagerank algorithm to prioritise workflow tasks. A min-max normalization method is applied to select the target data center and match the timeline at this data center. Additionally, a dynamic pagerank-constrained task scheduling algorithm is proposed to generate the task scheduling solution. The simulation results show that the proposed approach can achieve better fault handling performance, measured by task resilience ratio, workflow resilience ratio, and workflow continuity ratio in both the traditional 3-replica and the image backup cloud environment.

关键词： Cloud Performance Dependent Tasks Fault Handling pagerank algorithm Reactive Fault Tolerance Task Attributes Task Scheduling Timeline Allocation Workflow Scheduling

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Risk analysis of rainstorm-urban lifeline system disaster chain based on the pagerank-risk matrix and complex network

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NATURAL HAZARDS 2024年第12期120卷 10583-10606页

作者： Guo, Hai-xiang He, Xin-yu Lv, Xin-biao Wu, Yang China Univ Geosci Sch Econ & Management Wuhan Peoples R China China Univ Geosci Lab Nat Disaster Risk Prevent & Emergency Manageme Wuhan Peoples R China China Univ Geosci Inst Adv Studies Wuhan Peoples R China

Rainstorm disasters cause serious threats to people's lives and property. Enhancing emergency response and decision-making capabilities for rainstorm disasters is necessary. In this paper, 87 rainstorm disasters worldwide were first analysed, and secondary events across 15 urban lifeline systems were summarized. Based on the obtained findings and the characteristics of rainstorm disaster evolution and complex network theory, a model of rainstorm disaster chains in urban lifeline systems was constructed, and both partial and overall analyses of this model were performed. With the use of the pagerank risk matrix method, quantitative node risk levels were calculated for different parts of the model, and complex network theory was applied to assess the overall chain risk. The results showed that the highest risk disaster chain was flood -> houses submerged or collapsed -> road damaged -> traffic congestion or paralysis. The most important node was traffic congestion or paralysis, underlining the acute need for emergency response measures in urban traffic systems during rainstorm disasters. Overall, this research provides a crucial direction for preventing rainstorm disasters in urban lifeline systems.

关键词： Rainstorm disaster Complex networks Risk matrix pagerank algorithm Disaster chain

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Extractive Arabic Text Summarization Using pagerank and Word Embedding

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 13115-13130页

作者： Alselwi, Ghadir Tasci, Tugrul Sakarya Univ Informat Syst Engn Esentepe Campus TR-54187 Serdivan Sakarya Turkiye

Research on graph-based automatic text summarization for Arabic, the official language of 26 nations with over 200 million speakers, as well as other prevalent languages, has recently increased due to the ability of these approaches to handle linguistic peculiarities such as complex morphological linkages. The present paper proposes a graph-based extractive Arabic text summarization (GEATS) technique that employs word embedding and pagerank algorithms for feature extraction and sentence ordering. The efficiency of the GEATS approach versus the state-of-the-art methods is analyzed based on the quality of the produced summaries over the F-measure values. The findings indicated that it outperformed the nearest alternative by an advantage of over 7.5%.

关键词： Extractive text summarization Arabic text summarization pagerank algorithm Word embedding Farasa Stemmer

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Distributed Randomized pagerank algorithms Based on Web Aggregation over Unreliable Channels

Distributed Randomized PageRank Algorithms Based on Web Aggr...

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2010 49th IEEE Conference on Decision and Control

作者： Hideaki Ishii Roberto Tempo Er-Wei Bai Department of Computational Intelligence and Systems Science Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8502 Japan IEIIT-CNR Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Department of Electrical and Computer Engineering The University of Iowa 4316 Seamans Center for the Engineering Arts and Sciences Iowa City IA 52242-1527 U.S.A.

ISBN: (纸本)9781424477456

The pagerank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own pagerank by communicating with linked pages. Here, we focus on the effects of unreliability in the communication and, in particular, model the random data losses as an outcome of Markov chains. By generalizing the aggregated pagerank computation previously developed, we provide a distributed scheme along with analyses on its convergence properties.

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Stochastic matrices Unreliable channels

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