In the last few years, a lot of research has been devoted to developing new techniques for improving the recall and precision of current Web search engines. Few works deal with the interesting problem of identifying the communities to which pages belong. Most previous approaches tried to cluster data by means of spectral techniques or traditional hierarchical algorithms. The main problem with these techniques is that they ignore the fact that Web communities are social networks with distinctive statistical properties. We analyze Web communities on the basis of the evolution of an initial set of hubs and authoritative pages. The evolution law captures the behaviour of page authors with respect to the popularity of existing pages for topics of interest. Assuming such a model, we have found interesting properties of Web communities and have proposed a technique for computing relevant properties for specific topics. Several experiments have confirmed the validity of both the model and the identification method.
['Gianluigi Greco', 'Sergio Greco', 'Ester Zumpano']
A stochastic approach for modeling and computing Web communities