Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
The stochastic approach for link-structure analysis (SALSA) and the TKC effect
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
From LP to LP: Programming with Constraints
TACS '91 Proceedings of the International Conference on Theoretical Aspects of Computer Software
Using non-linear dynamical systems for web searching and ranking
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools), Second Edition
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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The main algorithms at the heart of search engines have focused on ranking and classifying sites. This is appropriate when we know what we are looking for and want it directly. Alternatively, we surf, in which case ranking and classifying links becomes the focus. We address this problem using a latent semantic analysis of the web. This technique allows us to rate, suppress or create links giving us a version of the web suitable for surfing. Furthermore, we show on benchmark examples that the performance of search algorithms such as PageRank is substantially improved as they work on an appropriately weighted graph.