Page Ranking Algorithms: A Survey

Duhan, Neelam; Sharma, Anukrati; Bhatia, Komal Kumar

doi:10.1109/iadcc.2009.4809246

Cited by 98 publications

(36 citation statements)

References 7 publications

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“…• Hubs and authorities: It is not easy to distinguish between hubs and authorities because many sites are hubs as well as authorities • Topic drift: Sometime HITS may not produce the most relevant documents to the user queries because of equivalent weights • Automatically generated links: HITS gives equal importance for automatically generated links which may not produce relevant topics for the user query • Efficiency: HITS algorithm is not efficient in real time • Table 2 shows the comparison (Duhan et al, 2009) of all the algorithms discussed above…”

Section: Results Analysismentioning

confidence: 99%

Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval

Kumar¹

2010

American Journal of Applied Sciences

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Problem statement:A study on hyperlink analysis and the algorithms used for link analysis in the Web Information retrieval was done. Approach: This research was initiated because of the dependability of search engines for information retrieval in the web. Understand the web structure mining and determine the importance of hyperlink in web information retrieval particularly using the Google Search engine. Hyperlink analysis was important methodology used by famous search engine Google to rank the pages. Results: The different algorithms used for link analysis like PageRank (PR), Weighted PageRank (WPR) and Hyperlink-Induced Topic Search (HITS) algorithms are discussed and compared. PageRank algorithm was implemented using a Java program and the convergence of the PageRank values are shown in a chart form. Conclusion: This study was done basically to explore the link structure algorithms for ranking and compare those algorithms. The further research on this area will be problems facing PageRank algorithm and how to handle those problems.

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Section: Results Analysismentioning

confidence: 99%

Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval

Kumar¹

2010

American Journal of Applied Sciences

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“…In paper [5] the author gives the information about the working process of page rank and HITS both. The most important feature of HITS is the mutual reinforcement between hubs and authorities, while the most important feature of Page Rank is the hyperlink weight normalization.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Link Analysis Extension of Correspondence Analysis to Mine Web Pages

2017

IJSR

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“…The hub page serves as an organizer of information on a given topic and points to many good authority pages on the particular subject. When a user comes to a hub page, he or she will find many useful links to the best pages on the subject [2,18]. Since its emergence, the HITS algorithm has undergone many improvements.…”

Section: Hitsmentioning

confidence: 99%

New use of the HITS algorithm for fast web page classification

Meadi¹,

Babahenini²,

Ahmed³

2017

Turk J Elec Eng & Comp Sci

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Abstract:The immense number of documents published on the web requires the utilization of automatic classifiers that allow organizing and obtaining information from these large resources. Typically, automatic web pages classifiers handle millions of web pages, tens of thousands of features, and hundreds of categories. Most of the classifiers use the vector space model to represent the dataset of web pages. The components of each vector are computed using the term frequency inversed document frequency (TFIDF) scheme. Unfortunately, TFIDF-based classifiers face the problem of the large-scale size of input data that leads to a long processing time and an increase in resource requests. Therefore, there is an increasing demand to alleviate these problems by reducing the size of the input data without influencing the classification results. In this paper, we propose a novel approach that improves web page classifiers by reducing the size of the input data (i.e. web pages and feature reduction) by using the hypertext induced topic search (HITS) algorithm.We employ HITS results for weighting remaining features. We evaluate the performance of the proposed approach by comparing it with the TFIDF-based classifier. We demonstrate that our approach significantly reduces the time needed for classification.

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Page Ranking Algorithms: A Survey

Cited by 98 publications

References 7 publications

Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval

Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval

A Survey on Link Analysis Extension of Correspondence Analysis to Mine Web Pages

New use of the HITS algorithm for fast web page classification

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