BLOGRANK：基于连通性和相似性特征的Weblog排序
BLOGRANK: Ranking Weblogs Based On Connectivity And Similarity Features
摘要：
大部分隐藏的网络都驻留在博客服务器中。新的内容是在每天的基础上产生和传统的搜索引擎的工作变成是不够的，由于性质的博客。本文总结了博客世界的结构，并突出了weblog的特殊性。在本文中，我们提出了一种方法来排名的基础上的链接图和几个相似性特征的博客。首先，我们创建一个增强的连接博客图，并添加新类型的边缘和权重，利用许多博客功能。然后，我们使用我们的算法，BlogRank，这是PageRank的修改版本，为每个博客分配一个排名。为了验证我们的方法，我们运行实验的网络日志数据集，我们处理和适应我们的搜索引擎。(http：//spiderwave.aueb.gr/Blogwave）。结果表明，使用增强图和BlogRank算法是用户的首选。
英文摘要：
A large part of the hidden web resides in weblog servers. New content is produced in a daily basis and the work of traditional search engines turns to be insufficient due to the nature of weblogs. This work summarizes the structure of the blogosphere and highlights the special features of weblogs. In this paper we present a method for ranking weblogs based on the link graph and on several similarity characteristics between weblogs. First we create an enhanced graph of connected weblogs and add new types of edges and weights utilising many weblog features. Then, we assign a ranking to each weblog using our algorithm, BlogRank, which is a modified version of PageRank. For the validation of our method we run experiments on a weblog dataset, which we process and adapt to our search engine. (http://spiderwave.aueb.gr/Blogwave). The results suggest that the use of the enhanced graph and the BlogRank algorithm is preferred by the users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
