Page Ranking Algorithms in Web Mining, Limitations of Existing Methods and a New Method for Indexing Web Pages

Jain, P.K.; Sharma, Richa; Dixit, Gireesh; Tomar, Vikrant Singh

doi:10.1109/csnt.2013.137

Cited by 34 publications

(7 citation statements)

References 17 publications

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“…Whenever a web page was accessed the script will be loaded on client side from the web server. Script would take care of the click as well as keyboard event to occur [12]. When an event occur and if that event will happen over hyperlink then it will send a message to web server with information of current web page and hyperlink.…”

Section: Page Rank Based On Visits Of Linksmentioning

confidence: 99%

Nascent Weighted Page Rank Algorithm

Kumar¹,

Kumar²

2017

IJCA

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There may be a millions of web pages that include a particular words or specific phrases. However some of them will be more relevant and popular than others. Modern search engines apply methods of ranking the results to present the best results first after that just plain text searching. The main objective of this paper is to explain the various existing page ranking algorithms and the enhancement done to the standard page rank algorithm. The weighted page rank algorithm based on visits of links by user is enhanced and a new algorithm called Nascent Weighted Page Rank (NWPR) algorithm is proposed. The proposed algorithm considers the additional factor of weight due to outlinking pages in spite of weight due to inlinking pages and the visits of links by user in calculating the page rank. It is observed that the results of the proposed algorithm are comparable to the previously known algorithms. Also the value of page ranks of web pages computed by the NWPR is largely dependent on the value of d (damping factor).

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Section: Page Rank Based On Visits Of Linksmentioning

confidence: 99%

Nascent Weighted Page Rank Algorithm

Kumar¹,

Kumar²

2017

IJCA

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“…The LP is the total number of sessions that a component appears in. GP measures some level of impact assessment, which could be link-based algorithms that identify how well referenced sites are by other sites, such as PageRank, HITS and CLEVER [14], or the impact assessment could be based upon the analysis of the volume of web traffic, such as in Alexa Internet [22]. Here, we use the GP rank metric from the Alexa Internet Traffic Rank, which provides a global metric for a substantial number of websites.…”

Section: Relative Popularitymentioning

confidence: 99%

Towards Web Usage Attribution via Graph Community Detection in Grouped Internet Connection Records

Gresty

Loukas

Gan

et al. 2017

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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Internet connection records can be very useful to digital forensic analysts in producing Internet history timelines and making deductions about the cause and effect of activity. However, the available data may include only a subset of the data that would be available from physical extraction. For example, the new UK legislation allows the collection of host website details, time of access and subscriber details, but not the specific uniform resource locator visited. Here, we investigate how to process data from Internet connections records to extract the websites, and construct the sessions of activity that are likely to be idiosyncratic the individual users, from the set of multiple possible users. We demonstrate how to display Internet history sessions as a network and perform graph community detection, showing a scheme for breaking up the component parts of the Internet history sessions into groups. We also introduce the use of websites' relative popularity for identifying websites that are likely to be meaningful to particular users of particular devices, further improving the accuracy of attributing a particular activity session to a particular user at a particular point in time.

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“…PageRank considers the structure of the social network, but it ignores the social interaction properties. So this paper proposes a Leader-PageRank algorithm, based on the combination of sentiment analysis and weighted PageRank algorithm [11].…”

Section: Social Network Leader-pagerank Algorithmmentioning

confidence: 99%

Identification of Opinion Leaders in Social Networks Based on Sentiment Analysis: Evidence from an Automotive Forum

Zhu¹,

Lin²,

Lü³

et al. 2016

Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)

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Opinion leadership of social network plays an important role in the fields of knowledge spreading, public sentiment controlling, marketing, etc. The opinions of users are derived from the reviews of topics, and the analysis of users' sentiment is helpful in recognizing users' emotional preference of opinion leaders. Thus, it is necessary to classify the forum posts and refine the posts with highly professional knowledge. We then improve the sentiment analysis due to the imbalanced datasets, and establish a comprehensive attention and emotion weight matrix. Accordingly, in this paper, we are going to propose a Leader-PageRank algorithm, which is based on the social network structure and emotional tendency. We do the comparative experiments on the automotive forum, and the results show that the Leader-PageRank algorithm can identify the positive opinion leaders in the professional fields effectively through connecting with the interactions in social networks.

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Page Ranking Algorithms in Web Mining, Limitations of Existing Methods and a New Method for Indexing Web Pages

Cited by 34 publications

References 17 publications

Nascent Weighted Page Rank Algorithm

Nascent Weighted Page Rank Algorithm

Towards Web Usage Attribution via Graph Community Detection in Grouped Internet Connection Records

Identification of Opinion Leaders in Social Networks Based on Sentiment Analysis: Evidence from an Automotive Forum

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