Visualizing the structure of Web communities based on data acquired from a search engine
Visualizing the structure of Web communities based on data acquired from a search engine
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根据从搜索引擎获取的数据可视化网络社区的结构
DOI：
10.1109/tie.2003.817486
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
T. Murata
中科院分区：
文献类型：
--
作者：
T. Murata
发现Web社区（共享共同兴趣的Web页面组）对于帮助用户从Web检索信息非常重要。本文描述了一种可视化Web社区及其内部结构的方法。以图形形式将Web社区可视化，使用户能够方便地访问相关页面，往往反映了Web社区的特点。由于相关的Web页面经常从同一Web页面中被共同引用，因此在搜索引擎中引用的共同出现次数用于度量页面之间的关系。将两个URL作为关键字提供给搜索引擎，然后计算从两个URL搜索的页面数除以从其中一个URL搜索的页面数的值，即Jaccard系数，作为评估两个URL之间关系的标准。该值用于确定图中边的长度，以便相关页面的顶点彼此靠近。我们基于该方法的可视化系统成功地阐明了各种类型的Web社区，尽管该系统不解释页面的内容。计算Jaccard系数的方法易于计算机系统处理，并且适合使用从搜索引擎获取的数据进行可视化。
Discovery of Web communities, groups of Web pages sharing common interests, is important for assisting users' information retrieval from the Web. This paper describes a method for visualizing Web communities and their internal structures. visualization of Web communities in the form of graphs enables users to access related pages easily, and it often reflects the characteristics of the Web communities. Since related Web pages are often co-referred from the same Web page, the number of co-occurrences of references in a search engine is used for measuring the relation among pages. Two URLs are given to a search engine as keywords, and the value of the number of pages searched from both URLs divided by the number of pages searched from either URL, which is called the Jaccard coefficient, is calculated as the criteria for evaluating the relation between the two URLs. The value is used for determining the length of an edge in a graph so that vertices of related pages will be located close to each other. Our visualization system based on the method succeeds in clarifying various genres of Web communities, although the system does not interpret the contents of the pages. The method of calculating the Jaccard coefficient is easily processed by computer systems, and it is suitable for visualization using the data acquired from a search engine.