Topical TrustRank

Wu, Baoning; Goel, Vinay; Davison, Brian D.

doi:10.1145/1135777.1135792

Cited by 106 publications

(6 citation statements)

References 15 publications

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“…Several results has appeared that apply rank propagation to extend initial trust or distrust judgments over a small set of seed pages or sites to the entire web, such as trust [53,103], distrust [81,32] propagation in the neighborhood or their combination [102] as well as graph based similarity measures [9]. These methods are either based on propagating trust forward or distrust backwards along the hyperlinks based on the idea that honest pages predominantly point to honest ones, or, stated the other way, spam pages are pointed by spam pages.…”

Section: Related Resultsmentioning

confidence: 99%

Assesing the quality of Web via semi-supervised methods

Siklósi¹

2016

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Section: Related Resultsmentioning

confidence: 99%

Assesing the quality of Web via semi-supervised methods

Siklósi¹

2016

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“…Then, Wu et al 16 introduced the cheating methods of hidden content and redirection to supplement the new cheating methods. Because TrustRank selects a seed set that does not necessarily effectively represent different topics on the Web, and TrustRank tends to favor larger communities, Wu et al 17 proposed topic‐based TrustRank in 2006, which divides the seed set by topic information, calculates trust scores for each topic, reduces the rank of web spam by spreading trust and nontrust scores simultaneously, 18 and cheats links by random walk 19I n. 2009, Zhang et al 20 used bidirectional links to pass AVRank and HVRank ratings, and achieved better results than TrustRank in identifying high‐quality webpages.…”

Section: Related Workmentioning

confidence: 99%

Adaptive evaluation model of web spam based on link relation

Wang

Han

Zhou

et al. 2020

Trans Emerging Tel Tech

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The credibility evaluation of web pages is an important part of current network information mining. Generally, content‐based evaluation and link relationship‐based evaluation are used to detect web spam. This paper proposes an algorithm based on link relationship which adjusts the weight of the link object according to the credibility of the link object to calculate the credibility of the web page. Considering the different linking conditions, (i) low‐scoring web pages actively linking to high‐scoring web pages, resulting in the reduction of high‐scoring web pages score; (ii) in order to endorse the low‐score web page, high‐score web pages actively linking to the low‐score web pages, adjust the score transmission. Obviously, the proposed algorithm is a recursive model. To avoid the amplification of the error caused by the loop and different evaluation results caused by different initial nodes, this paper presents a novel method to adaptively adjust the weights of the links according to the link conditions, then proves the convergence of the algorithm. Finally, we validate the proposed algorithm on the public datasets, which is compared with other up‐to‐date algorithms under different experimental indexes. The results show that the proposed algorithm can effectively reduce the ranks of spam pages in all web pages and improve the detection efficiency of spam pages.

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“…In [19], Wu et al propose an approach based on trust and distrust propagation. This work consists in an algorithm that computes two scores for each node in the graph, indicating the levels of trust and distrust of a page.…”

Section: Related Workmentioning

confidence: 99%

Spam detection with a content-based random-walk algorithm

Ortega

Macdonald

Troyano

et al. 2010

Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents

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In this work we tackle the problem of the spam detection on the Web. Spam web pages have become a problem for Web search engines, due to the negative effects that this phe-nomenon can cause in their retrieval results. Our approach is based on a random-walk algorithm that obtains a ranking of pages according to their relevance and their spam likelihood. We introduce the novelty of taking into account the content of the web pages to characterize the web graph and to ob-tain an apriori estimation of the spam likekihood of the web pages. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) is a web page, according to its textual content and the relations in the graph. Our experiments show that our proposed technique outperforms other link-based techniques for spam detection.

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Topical TrustRank

Cited by 106 publications

References 15 publications

Assesing the quality of Web via semi-supervised methods

Assesing the quality of Web via semi-supervised methods

Adaptive evaluation model of web spam based on link relation

Spam detection with a content-based random-walk algorithm

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