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Effects of maximum flow algorithm on identifying web community


Noriko Imafuji and Masaru Kitsuregawa

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Abstract

In this paper, we describe the effects of using maximum flow algorithm on extracting web community from the web. A web community is a set of web pages having a common topic. Since the web can be recognized as a graph that consists of nodes and edges that represent web pages and hyperlinks respectively, so far various graph theoretical approaches have been proposed to extract web communities from the web graph. The method of finding a web community using maximum flow algorithm was proposed by NEC Research Institute in Princeton two years ago. However the properties of web communities derived by this method have been seldom known. To examine the effects of this method, we selected 30 topics randomly and experimented using Japanese web archives crawled in 2000. Through these experiments, it became clear that the method has both advantages and disadvantages. We will describe some strategies to use this method effectively. Moreover, by using same topics, we examined another method that is based on complete bipartite graphs. We compared the web communities obtained by those methods and analyzed those characteristics.


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