Modeling the web as a hypergraph to compute page reputation

Berlt, Klessius; Moura, Edleno Silva de; Carvalho, Andre L.; Cristo, Marco; Ziviani, Nívio; Couto, Thierson

doi:10.1016/j.is.2009.02.005

Cited by 16 publications

(33 citation statements)

References 17 publications

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“…where rðpÞ is the PageRank value for a Web page p; wðqÞ is the number of forward links on the page q; rðpÞ is the PageRank of page q; N is the total number of Web pages in the Web; a is the damping factor; ðq; pÞ means that Web page q points to Web page p (Berlt et al, 2010). A Web page with a high PageRank score will appear at the top of the list of SEPR as a response to a particular query.…”

Section: Pagerank Algorithmmentioning

confidence: 99%

Evaluation of Spam Impact on Arabic Websites Popularity

Al-Kabi

Alsmadi

Wahsheh

2015

Journal of King Saud University - Computer and Information Scie

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The expansion of the Web and its information in all aspects of life raises the concern of how to trust information published on the Web especially in cases where publisher may not be known. Websites strive to be more popular and make themselves visible to search engines and eventually to users. Website popularity can be measured using several metrics such as the Web traffic (e.g. Website: visitors' number and visited page number). A link or page popularity refers to the total number of hyperlinks referring to a certain Web page. In this study, several top ranked Arabic Websites are selected for evaluating possible Web spam behavior. Websites use spam techniques to boost their ranks within Search Engine Results Page (SERP). Results of this study showed that some of these popular Websites are using techniques that are considered spam techniques according to Search Engine Optimization guidelines. ª 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Pagerank Algorithmmentioning

confidence: 99%

Evaluation of Spam Impact on Arabic Websites Popularity

Al-Kabi

Alsmadi

Wahsheh

2015

Journal of King Saud University - Computer and Information Scie

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“…Therefore, some variants of traditional algorithms have been proposed to remove or unweight these links. In the following paragraphs, we describe a family of algorithms for link analysis that incorporate robust strategies to disposal links [3].…”

Section: Link Analysismentioning

confidence: 99%

How do metrics of link analysis correlate to quality, relevance and popularity in wikipedia?

Hanada

Cristo

Pimentel

2013

Proceedings of the 19th Brazilian Symposium on Multimedia and the Web

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Many links between Web pages can be viewed as indicative of the quality and importance of the pages they pointed to. Accordingly, several studies have proposed metrics based on links to infer web page content quality. However, as far as we know, the only work that has examined the correlation between such metrics and content quality consisted of a limited study that left many open questions. Despite the fact that these metrics showed to be successful in the task of ranking pages provided as answers to queries submitted to search engines, it is not possible to determine the specific contribution that factors such as quality, popularity, and importance have on the results. This difficulty is partially due to the fact that such information about Web pages in general is hard to obtain. Unlike ordinary Web pages, the quality, importance and popularity of Wikipedia articles are evaluated by human experts or might be easily estimated. Thus, it is feasible to verify the relationship between link analysis metrics and the afore mentioned factors in Wikipedia articles. This is our goal in this work. In order to accomplish that, we implemented several link analysis algorithms and compared their resulting rankings with the ones created by human evaluators regarding factors such as quality, popularity and importance. We found that the metrics are more correlated to quality and popularity than to importance, and that the correlations are moderate.

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“…In this model the users, the resources and the social networks of an SIS are represented as nodes of a hypergraph and the corresponding actions and relationships are represented as hyperedges of the same hypergraph [10]. This model is designed in such a way as to represent also user, resource and social network profiles as well as to capture simultaneously a large variety of actions that users can carry out on an SIS.…”

Section: General Characteristics Of Our Approachmentioning

confidence: 99%