Relational link-based ranking
Faculty of Sciences. Mathematics and Computer Science
San Francisco, Calif. :Morgan Kaufmann, 2004
(e)Proceedings of the 30th International Conference on Very Large Data Bases (VLDB 2004), Toronto, Canada, August 31 - September 3, 2004 / Nascimento, Mario A. [edit.]
Link analysis methods show that the intercon- nections between web pages have lots of valu- able information. The link analysis methods are, however, inherently oriented towards an- alyzing binary relations. We consider the question of generalizing link analysis methods for analyzing relational databases. To this aim, we provide a general- ized ranking framework and address its prac- tical implications. More speci¯cally, we associate with each rela- tional database and set of queries a unique weighted directed graph, which we call the database graph. We explore the properties of database graphs. In analogy to link analysis algorithms, which use the Web graph to rank web pages, we use the database graph to rank partial tuples. In this way we can, e.g., ex- tend the PageRank link analysis algorithm to relational databases and give this extension a random querier interpretation. Similarly, we extend the HITS link analysis al- gorithm to relational databases. We conclude with some preliminary experimental results.