Masashi Toyoda
Masaru Kitsuregawa
ABSTRACT
Recent research on link analysis has shown the existence of numerous web communities on the Web. A web community is a collection of web pages created by individuals or any kind of associations that have a common interest on a specific topic. In this paper, we propose a technique to create a web community chart, that connects related web communities, from thousands of seed pages. This allows the user to navigate through related web communities, and can be used for a `What's Related Community' service that provides not only the web community including a given page but also related web communities. Our technique is based on a related page algorithm that gives related pages to a given page using only link analysis. We show that the algorithm can be used for creating the chart by applying the algorithm to each seed, then using similarities of the results to classify seeds into clusters and to deduce their relationships. We perform experiments to create a web community chart of companies and organizations from thousands of seed pages. First, we improve the precision of an existing related page algorithm, Companion, and evaluated the improved version, Companion-, by an user study. Then the chart is created using Companion-. The result chart consists of web communities including related pages, and paths between related web communities. From the chart, we can find many web communities of companies classified by their category of business, and relationships between the communities.
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Index Terms
- Creating a Web community chart for navigating related communities
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