scholarly journals Comparative Analysis of Ranking Algorithms Used On Web

2020 ◽  
Vol 4 (2) ◽  
pp. 14-25 ◽  
Author(s):  
Sandeep Suri ◽  
Arushi Gupta ◽  
Kapil Sharma
Keyword(s):  
Web Search ◽  
Ranking Algorithm ◽  
Web Information Retrieval ◽  
Huge Amount ◽  
Page Rank ◽  
Ranking Algorithms ◽  
Page Ranking ◽  
Specific Manner ◽  
Search Tool ◽  
Web Crawlers

With the evolution in technology huge amount of data is being generated, and extracts the necessary data from large volumes of data. This process is significantly complex. Generally the web contains bulk of raw data and the process of converting this data to information mining process can be performed. At whatever point the user places some inquiry on particular web search tool, outcomes are produced with respect to the requests which are dependent on the magnitude of the document created via web information retrieval tools. The results are obtained using calculations and implementation of well written algorithms. Well known web search tools like Google and other varied engines contain their specific manner to compute the page rank, various outcomes are obtained on various web crawlers for a same inquiry because the method for deciding the importance of the sites contrasts among number of algorithm. In this research, an attempt to analyze well-known page ranking calculation on the basis of their quality and shortcomings. This paper places the light on a portion of the extremely mainstream ranking algorithm and attempts to discover a better arrangement that can optimize the time spent on looking through the list of sites.

scholarly journals Search Engine-inspired Ranking Algorithm for Trading Networks

2018 ◽  
Vol 9 (3) ◽  
pp. 812
Author(s):  
Andri Mirzal
Keyword(s):  
United Nations ◽  
Web Search ◽  
Similarity Measures ◽  
Ranking Algorithm ◽  
Link Structure ◽  
Ranking Algorithms ◽  
International Trading ◽  
Trading Network ◽  
Trading Networks

<p>Ranking algorithms based on link structure of the network are well-known methods in web search engines to improve the quality of the searches. The most famous ones are PageRank and HITS. PageRank uses probability of random surfers to visit a page as the score of that page, and HITS instead of produces one score, proposes using two scores, authority and hub scores, where the authority scores describe the degree of popularity of pages and hub scores describe the quality of hyperlinks on pages. In this paper, we show the differences between WWW network and trading network, and use these differences to create a ranking algorithm for trading networks. We test our proposed method with international trading data from United Nations. The similarity measures between vectors of proposed algorithm and vector of standard measure give promising results.</p>

scholarly journals A Comparative Analysis of Search Engine Ranking Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 1247-1252
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Ranking Algorithm ◽  
Video Content ◽  
Uniform Resource Locator ◽  
Page Rank ◽  
Ranking Algorithms ◽  
Content Search ◽  
Google Search ◽  
Better Than

Ranking Algorithm is the most proper way of positioning on a scale. As the information and knowledge on the internet are increasing every day.The search engine's ability to deliver the most appropriate material to the customer. It is more and more challenging without even any assistance in filtering through all of it. However, searching what user requires is extremely difficult. In this research, an effort has been made to compare and analyze the most popular and effective search engines. The keywords were used in uniform resource locator like, title tag, header, or even the keyword's resembles to the actual text. The page rank algorithm computes a perfect judgment of how relevant a webpage is by analyzing the quality and calculating the number of links connected to it. In this study the keyword relevancy and time response were used for search engines and observed the results. It is observed that the google search engine is faster than the bing and youtube, and after all, bing is the best search engine after google. Moreover, youtube is the fastest search engine in terms of video content search. The google results were found more accurate. However, it is better than all of the search engine

scholarly journals Optimizing webpage relevancy using page ranking and content based ranking

2018 ◽  
Vol 7 (2.7) ◽  
pp. 1025
Author(s):  
J Satish Babu ◽  
T Ravi Kumar ◽  
Dr Shahana Bano
Keyword(s):  
Web Search ◽  
Neural System ◽  
Order Structure ◽  
Web Content ◽  
Mining System ◽  
Web Content Mining ◽  
Page Ranking ◽  
Web Structure Mining ◽  
Content Mining ◽  
Search Tool

Systems for web information mining can be isolated into a few classifications as indicated by a sort of mined data and objectives that specif-ic classifications set: Web structure mining, Web utilization mining, and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning taking into account the page content investigation. The strategy, we call it Page Content Rank (PCR) in the paper, consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the significance of terms which the page contains. The significance of a term is determined concern-ing a given inquiry q and it depends on its measurable and linguistic elements. As a source set of pages for mining we utilize an arrangement of pages reacted by a web search tool to the question q. PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework –page rank algorithm.  

A Novel Page Ranking Algorithm Based on Social Annotations

2010 ◽  
Vol 33 (6) ◽  
pp. 1014-1023 ◽  
Author(s):  
Kai-Peng LIU ◽  
Bin-Xing FANG
Keyword(s):  
Ranking Algorithm ◽  
Page Ranking ◽  
Social Annotations

Network search

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Web Search ◽  
Distributed Databases ◽  
Small World ◽  
Peer Networks ◽  
Model Parameters ◽  
Breadth First Search ◽  
Ranking Algorithms ◽  
Network Search ◽  
Peer To Peer Networks ◽  
Search Processes

This chapter gives a discussion of search processes on networks. It begins with a discussion of web search, including crawlers and web ranking algorithms such as PageRank. Search in distributed databases such as peer-to-peer networks is also discussed, including simple breadth-first search style algorithms and more advanced “supernode” approaches. Finally, network navigation is discussed at some length, motivated by consideration of Milgram's letter passing experiment. Kleinberg's variant of the small-world model is introduced and it is shown that efficient navigation is possible only for certain values of the model parameters. Similar results are also derived for the hierarchical model of Watts et al.

Web Page Ranking Algorithm Based on the Meta-Information

2014 ◽  
Vol 596 ◽  
pp. 292-296
Author(s):  
Xin Li Li
Keyword(s):  
Experimental Results ◽  
High Rank ◽  
Ranking Algorithm ◽  
Web Page ◽  
Page Ranking ◽  
Two Factors ◽  
Meta Information ◽  
The Web

PageRank algorithms only consider hyperlink information, without other page information such as page hits frequency, page update time and web page category. Therefore, the algorithms rank a lot of advertising pages and old pages pretty high and can’t meet the users' needs. This paper further studies the page meta-information such as category, page hits frequency and page update time. The Web page with high hits frequency and with smaller age should get a high rank, while the above two factors are more or less dependent on page category. Experimental results show that the algorithm has good results.

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