论文标题
Pagerank，Cheirank和2drank在多语言网络中使用社会影响
Novel version of PageRank, CheiRank and 2DRank for Wikipedia in Multilingual Network using Social Impact
论文作者
论文摘要
如今，描述互联网用户导航行为的信息已用于多个领域，电子商务，经济，社会学和数据科学。这些信息可以从不同的知识库中提取，包括面向业务的知识库。在本文中，我们根据使用ClickStream和Google Matrix Construction中使用ClickStream和PageViews数据的Pagerank，Cheirank和2drank算法提出了一个新模型。我们使用了来自Wikipedia的数据，并分析了来自11个语言版本的2000万篇文章之间的链接。我们从SQL垃圾场中提取了超过14亿个源用餐对，并从XML垃圾场中提取了超过7亿对。此外，我们根据重定向页面的分析统一了对，并删除了所有重复项。此外，我们还基于所有考虑的语言版本创建了更大的Wikipedia文章网络，并获得了多语言措施。基于实际数据，我们讨论了标准的Pagerank，Cheirank，2drank和根据我们的方法和Wikipedia的多语言网络获得的方法之间的差异。
Nowadays, information describing navigation behaviour of internet users are used in several fields, e-commerce, economy, sociology and data science. Such information can be extracted from different knowledge bases, including business-oriented ones. In this paper, we propose a new model for the PageRank, CheiRank and 2DRank algorithm based on the use of clickstream and pageviews data in the google matrix construction. We used data from Wikipedia and analysed links between over 20 million articles from 11 language editions. We extracted over 1.4 billion source-destination pairs of articles from SQL dumps and more than 700 million pairs from XML dumps. Additionally, we unified the pairs based on the analysis of redirect pages and removed all duplicates. Moreover, we also created a bigger network of Wikipedia articles based on all considered language versions and obtained multilingual measures. Based on real data, we discussed the difference between standard PageRank, Cheirank, 2DRank and measures obtained based on our approach in separate languages and multilingual network of Wikipedia.
