A Site-Ranking Algorithm for a Small Group of Sites

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• 作者：KiJoo Kim ; MunSu Kang ; YoungSik Choi
• 关键词：Site Ranking ; PageRank ; Link Analysis ; Web Mining ; Information Retrieval
• 刊名：Lecture Notes in Computer Science
• 出版年：2007
• 出版时间：2007
• 年：2007
• 卷：4706
• 期：1
• 页码：397-405
• 全文大小：204 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Hyperlink, or shortly link, analysis seeks to model the web structures and discover the relations among web sites or Web pages. The extracted models or relations can be used for the web mining applications, including market researches and various online businesses. It is well known that PageRank of Google’s search engine is one of the most successful stories of link analysis. In this paper, we investigate into the link structures among the sites, each of which is the collection of web pages in the same university domain in Korea. However, the PageRank algorithm cannot be directly applied to the ranking of a relatively small number of sites or communities since the transition probabilities from a node with a low out-degree significantly affect the whole rankings among the sites. We modify the original version of the PageRank algorithm in order to make it fit into the site ranking, we propose a site ranking algorithm, which is a modification of the PageRank algorithm. The experimental results show that our approach to the site ranking performs much better than PageRank.