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Improved Subgraph Estimation pagerank algorithm for Web Page Rank

INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2015年第11期9卷 231-238页

作者： Li, Lanying Zhou, Qiuli Kong, Yin Dong, Yiming Harbin Univ Sci & Technol Coll Comp Sci & Technol Harbin 150080 Peoples R China

The traditional pagerank algorithm can't efficiently dispose large data Webpage scheduling problem. This paper proposes an accelerated algorithm named topK-Rank. It is based on pagerank on the MapReduce platform. Owing to this algorithm, Top k nodes can be found efficiently for a given graph without sacrificing accuracy. It can iteratively estimate lower/upper bounds of pagerank scores, and construct subgraphs in each iteration by pruning unnecessary nodes and edges. Theoretical analysis shows that this method guarantees result exactness. Experiments show that it can find top k nodes much faster than the existing approaches.

关键词： web data webpage scheduling pagerank algorithm MapReduce

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Research on Markov chain and pagerank algorithm

Research on Markov chain and PageRank algorithm

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International Conference on algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI)

作者： Kan, Tingyuan Jinan Foreign Language Sch Int Ctr Jinan Peoples R China

ISBN: (数字)9781510651890

ISBN: (纸本)9781510651890;9781510651883

The rapid growth of the amount of information on the Internet makes web search technology become the main means for people to obtain information. How to measure the importance of web pages efficiently and quickly has become an important topic, and has different degrees of influence on all aspects of the web page, such as web crawling, web page grading and ranking. This paper discusses an important algorithm-pagerank, based on Markov chain model then a discussion of the advantages and disadvantages of pagerank is provided here. Finally, according to the drawbacks, some corrections and improvements are also discussed in this paper.

关键词： Markov chain pagerank algorithm web retrieval Cosine similarity

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Business Information Mining Technology of Social Media Platform Based on pagerank algorithm 1

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6th European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Liu, Ying Jinggangshan Univ Sch Business Jian 343009 Peoples R China

ISBN: (数字)9783031288678

ISBN: (纸本)9783031288661;9783031288678

The existing methods of business information mining are flexible and cannot effectively mine the business information of media platform. In order to summarize and manage the mass business information effectively, a research method of business information mining based on pagerank algorithm for social media platform is proposed. Different from the existing methods, it innovatively optimizes the business information evaluation algorithm of social media platforms, increases the flexibility of information mining, and realizes the business information mining of social media platforms. The experiment proves that the technology of business information mining based on social media platforms can effectively summarize and manage a large amount of business information.

关键词： pagerank algorithm Social media Business information Data mining

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Expert Finding in Citizen Science Platform for Biodiversity Monitoring via Weighted pagerank algorithm 17th

Expert Finding in Citizen Science Platform for Biodiversity ...

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17th International Symposium on Intelligent Data Analysis (IDA)

作者： Saoud, Zakaria Fontaine, Colin Musum Natl Hist Nat Ctr Ecol & Sci Conservat UMR 7204 CNRS MNHN SU 61 Rue Buffon F-75005 Paris France

ISBN: (纸本)9783030017682;9783030017675

Numerous citizen science platforms aiming at monitoring biodiversity have emerged in the recent years. These platforms collect biodiversity data from participants and allow them to increase their scientific knowledge and share it with other participants, experts and scientists. One key aspect of such platforms is quality control on the data, a task usually performed by a limited number of co-opted experts. With the amount of data collected increasing steeply, finding new experts is needed. In this paper we propose a new graph-based expert finding approach for the citizen science platform SPIPOLL, aiming at collecting data on pollinator diversity across France. We exploit both users comments quality and users social relations to calculate users expertise for specific insect family. Experimental results show that the proposed method performs better than the state-of-the-art expert finding algorithms.

关键词： Expert finding pagerank algorithm Citizen sciences

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ANT pagerank algorithm

ANT PAGERANK ALGORITHM

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11th International Conference on Enterprise Information Systems

作者： Abdo, Mahmoud Zaki Ismail, Manal Ahmed Eladawy, Mohamed Ibraheem Helwan Univ Dept Commun Fac Engn Cairo Egypt

ISBN: (纸本)9789898111852

The amount of global information in the World Wide Web is growing at an incredible rate. Millions of results are returned from search engines. The rank of pages in the search engines is very important. One of the basic rank algorithms is pagerank algorithm. This paper proposes an enhancement of pagerank algorithm to speed up the computational process. The enhancement of pagerank algorithm depends on using the Ant algorithm. On average, this technique yields about 7.5 out of ten relevant pages to the query topic, and the total time reduced by 19.9 %.

关键词： Search engine pagerank algorithm Ranking algorithms Ant algorithm

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Structural Vulnerability Assessment of Multi-energy System Using a pagerank algorithm 10

Structural Vulnerability Assessment of Multi-energy System U...

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10th International Conference on Applied Energy (ICAE)

作者： Shen, Yichen Gu, Chenghong Zhao, Pengfei Univ Bath Bath BA2 7JY Avon England

Severe natural catastrophes may directly cause large-scale cascading failures in energy systems. This paper presents a comprehensive scheme of vulnerability assessment for multi-energy systems, including both electricity and natural gas. Differing from cascading failure theory, this scheme employs pagerank algorithm to measure the structural importance of the integrated system. A modified weighted pagerank methodology is applied to determine the importance of nodes in both natural gas and electricity networks. The effectiveness of this scheme is demonstrated and analyzed via an integrated energy system in the case study. This algorithm can help identify the weakness in the integrate energy system so that purposeful planning and operation strategies can be deployed to increase the security. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Vulnerability assessment Combined Gas-electric system pagerank algorithm

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TC-pagerank algorithm based on topic correlation

TC-PageRank algorithm based on topic correlation

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6th World Congress on Intelligent Control and Automation

作者： Huang, Decai Qi, Huachun Yuan, Yuan Zheng, Yue-Feng Zhejiang Univ Technol Coll Informat Engn Hangzhou 310032 Peoples R China

ISBN: (纸本)1424403316

pagerank algorithm is a famous algorithm to mine the web structure,but it has a drawback of topic-drift. To eliminate the topic-drift of the pagerank algorithm, and after the analysis of existing algorithms, a new algorithm called TC-pagerank algorithm is put forward. The TC-pagerank algorithm is based on, fictitious file vector and correlation measure of cosine. Experimental results illustrate that TC-pagerank algorithm eliminates the topic-drift phenomenon effectively, and thus improves the quality of retrieving.

关键词： hyperlink analysis pagerank algorithm topic correlation web structure mining

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pagerank algorithm-Based Recommendation System for Construction Safety Guidelines

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BUILDINGS 2024年第10期14卷 3041-3041页

作者： Lee, Jungwon Ahn, Seungjun Hongik Univ Dept Civil & Environm Engn Seoul 04066 South Korea

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The pagerank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on "High-rise Building Construction (General) Safety and Health Guidelines", using 'scaffolding' as the keyword, demonstrates the framework's effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as 'fall', 'drop', and 'scaffolding', with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management.

关键词： safety guidelines knowledge graph recommendation system pagerank algorithm

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An Efficient Practical Non-Blocking pagerank algorithm for Large Scale Graphs 29

An Efficient Practical Non-Blocking PageRank Algorithm for L...

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29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

作者： Eedi, Hemalatha Peri, Sathya Ranabothu, Neha Utkoor, Rahul Indian Inst Technol Hyderabad Dept Comp Sci & Engn Hyderabad 502285 Telangana India

ISBN: (纸本)9781665414555

pagerank algorithm is a benchmark for many graph analytics and is the underlying kernel for link predictions. recommendation systems. It is an iterative algorithm that updates ranks of pages until the value converges. Implementation of pagerank algorithm on a shared memory architecture while taking advantage of line-grained parallelism using large-scale graphs is a challenging task. In this paper. We present parallel algorithms for computing the pagerank suitable to the shared memory systems. Initially, we present parallel implementations of page-rank algorithms using harrier and lock variants. Later. we propose new approaches which are lock-free and are harrier-less synchronization to overcome the issues of lock based methods. A detailed experimental analysis of our approach is carried out using real-world web graphs from SNAP and Synthetic Graphs from RMAT on an Intel(R) Xeon E5-2660 v4 processor architecture with 56 threads using the POSIX thread library.

关键词： Shared Memory Architecture pagerank algorithm Fine-Grained Parallelism No Synchronization and Wait-Free

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A Comparative Analysis of Arabic Text Summarization Techniques: Evaluating Word Frequency, K-means Clustering, and pagerank algorithm 4

A Comparative Analysis of Arabic Text Summarization Techniqu...

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4th International Mobile, Intelligent, and Ubiquitous Computing Conference

作者： Karim, Abdelrhman A. Ibrahim, Manar A. Hosny, Mahmoud M. Ibrahim, Abdulrhman H. Mohamed, Reem A. Elbanbawy, Magy Al-Zoghby, Aya M. Foad, Khaled Moawad, Ibrahim Fathy New Mansoura Univ Fac Comp Sci & Engn Artificial Intelligence Engn Dakhlia Egypt New Mansoura Univ Fac Comp Sci & Engn Artificial Intelligence Sci Dakhlia Egypt Mansoura Univ Fac Engn Comp & Syst Dept Dakhlia Egypt New Mansoura Univ Fac Comp Sci & Engn Dakhlia Egypt Damietta Univ Fac Comp & Artificial Intelligence Comp Sci Dept Dumyat Egypt Benha Univ Fac Comp & Artificial Intelligence Informat Syst Banha Egypt Ain Shams Univ Fac Comp & Informat Sci Informat Syst Cairo Egypt Taibah Univ Coll Comp Sci & Engn Dept Artificial Intelligence Medina 42353 Saudi Arabia

ISBN: (纸本)9798350367782;9798350367775

This study explores methods to efficiently summarize extensive Arabic texts, addressing the growing need to condense large volumes of content across various fields. Three primary techniques are evaluated: Word Frequency Analysis, K-means Clustering based on Sentence Proximity, and the pagerank algorithm. The research finds the pagerank algorithm to be the most effective, delivering higher compression ratios while maintaining strong recall and precision metrics. In particular, the pagerank method achieved the highest compression ratio of 0.562 while maintaining a population standard deviation of 2.0, compared to other techniques. Evaluation metrics such as population standard deviation, F1 score, and compression ratio support these findings. The study also examines advanced approaches like fuzzy logic-based methods, transformers, and multi-document summarization, aiming to enhance Arabic text summarization.

关键词： Word frequency-based analysis Arabic text summarization Extractive techniques K-means clustering pagerank algorithm Unsupervised learning techniques

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