From whence does your authority come?: utilizing community relevance in ranking

Authors:
Lan Nie;Brian D. Davison;Baoning Wu
Affiliations:
Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA;Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA;Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Year:
2007

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Abstract

A web page may be relevant to multiple topics; even when nominally on a single topic, the page may attract attention (and thus links) from multiple communities. Instead of indiscriminately summing the authority provided by all pages, we decompose a web page into separate subnodes with respect to each community pointing to it. Utilizing the relevance of such communities allows us to better model the semantic structure of the Web, leading to better estimates of authority for a given query. We apply a total of eighty queries over two real-world datasets to demonstrate that the use of community decomposition can consistently and significantly improve upon Page-Rank's top-ten results.