  Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web
search, we propose a structural re-ranking approach to ad hoc information
retrieval: we reorder the documents in an initially retrieved set by exploiting
asymmetric relationships between them. Specifically, we consider generation
links, which indicate that the language model induced from one document assigns
high probability to the text of another; in doing so, we take care to prevent
bias against long documents. We study a number of re-ranking criteria based on
measures of centrality in the graphs formed by generation links, and show that
integrating centrality into standard language-model-based retrieval is quite
effective at improving precision at top ranks.
