Cycle detection in graphs and digraphs has received wide attention and several algorithms are available for this purpose. While the Web may be modeled as a digraph, such algorithms would not be of much use due to both the scale of the Web and the number of uninteresting cycles and sequences in it. We propose a novel sequence detection algorithm for Web pages, and highlight its importance for search related systems. Here, the sequence found is such that its consecutive elements have the same relation among them. This relation is measured in terms of the positional properties of navigational links, for which we provide a method for identifying navigational links. The proposed methodology does not detect all possible sequences and cycles in the Web graph, but just those that were intended by the creators of those Web pages. Experimental results confirm the accuracy of the proposed algorithm.

A novel Web surfer model, where the transitions between Web pages are fuzzy quantities, is proposed in this article. Such a model is appropriate when the links between pages are imprecise. The theoretical aspects of modeling the uncertainty associated with links are discussed. The advantages and limitations of the proposed methodology, which is based on the theory of fuzzy Markov chains, are described. Situations where fuzzy Web surfer models are appropriate compared to existing models are highlighted.

This paper describes a surfer model which incorporates information about topic continuity derived from the surfer's history. Therefore, unlike earlier models, it captures the interrelationship between categorization (context) and ranking of Web documents simultaneously. The model is mathematically formulated. A scalable and convergent iterative procedure is provided for its implementation. Its different characteristic features, as obtained from the joint probability matrix, and their significance in Web intelligence are mentioned. Experiments performed on Web pages obtained from WebBase confirm the superiority of the model.

The research activities of the WIC-India Research Center include topics like improving the performance of search engines, link and neighborhood analysis, as well as, surfer modeling for ranking and categorization of web pages, and query answering. In solving several web mining tasks, both statistical and soft computing tools are employed. Besides, some international collaborations and establishing a center of excellence are going on.

PageRank is primarily based on link structure analysis. Recently, it has been shown that content information can be utilized to improve link analysis. We propose a novel algorithm that harnesses the information contained in the history of a surfer to determine the topic of interest on a given page. As the history is unavailable until query time, we guess it probabilistically so that the operations can be performed offline. This leads to a better Web page categorization and, thereby, to a better ranking of Web pages.