Trustworthy Website Detection Based on Social Hyperlink Network Analysis
Trustworthy Website Detection Based on Social Hyperlink Network Analysis
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DOI：
10.1109/tnse.2018.2866066
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发表时间：
2020-01
影响因子：
6.6
通讯作者：
Xiaofei Niu;Guangchi Liu;Q. Yang
中科院分区：
文献类型：
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作者：
Xiaofei Niu;Guangchi Liu;Q. Yang
可信网站检测在从搜索引擎为用户提供有意义的网页方面起着重要作用。然而，目前针对这个问题的解决方案主要集中在检测垃圾网站，而不是推广更值得信赖的网站。在本文中，我们提出了增强的OpinionWalk（EOW）算法来计算所有网站的可信度，并确定具有较高信任值的可信网站。EOW算法将网站的超链接结构视为一个社会网络，并应用社会信任分析来计算单个网站的可信度。为了将社会信任分析和可信网站检测相结合，我们根据网站指向的网站数量和质量对网站的可信度进行建模。我们进一步在EOW中设计了一种机制，在算法“行走”网络时记录哪些网站的可信度需要更新。因此，与OpinionWalk算法相比，EOW的执行减少了27.1%。利用公共数据集WEBSPAM-UK 2006对EOW算法进行了验证，分析了种子选择、种子集大小、最大搜索深度和起始节点数对算法的影响。实验结果表明，EOW算法识别出的可信网站比TrustRank多5.35%到16.5%。
Trustworthy website detection plays an important role in providing users with meaningful web pages, from a search engine. Current solutions to this problem, however, mainly focus on detecting spam websites, instead of promoting more trustworthy ones. In this paper, we propose the enhanced OpinionWalk (EOW) algorithm to compute the trustworthiness of all websites and identify trustworthy websites with higher trust values. The proposed EOW algorithm treats the hyperlink structure of websites as a social network and applies social trust analysis to calculate the trustworthiness of individual websites. To mingle social trust analysis and trustworthy website detection, we model the trustworthiness of a website based on the quantity and quality of websites it points to. We further design a mechanism in EOW to record which websites’ trustworthiness need to be updated while the algorithm “walks” through the network. As a result, the execution of EOW is reduced by 27.1 percent, compared to the OpinionWalk algorithm. Using the public dataset, WEBSPAM-UK2006, we validate the EOW algorithm and analyze the impacts of seed selection, size of seed set, maximum searching depth and starting nodes, on the algorithm. Experimental results indicate that EOW algorithm identifies 5.35 to 16.5 percent more trustworthy websites, compared to TrustRank.