Graph Local Clustering for Topic Detection in Web Collections

In the midst of a developing Web that increases its size with a constant rhythm, automatic document organization becomes important. One way to arrange documents is by categorizing them into topics. Even when there are different forms to consider topics and their extraction, a practical option is to view them as document groups and apply clustering algorithms. An attractive alternative that naturally copes with the Web size and complexity is the one proposed by graph local clustering (GLC) methods. In this paper, we define a formal framework for working with topics in hyperlinked environments and analyze the feasibility of GLC for this task. We performed tests over an important Web collection, namely Wikipedia, and our results, which were validated using various kinds of methods (some of them specific for the information domain), indicate that this approach is suitable for topic discovery.