Separate and inequal: preserving heterogeneity in topical authority flows.

Web pages, like people, are often known by others in a variety of contexts. When those contexts are sufficiently distinct, a page's importance may be better represented by multiple domains of authority, rather than by one that indiscriminately mixes reputations. In this work we determine domains of authority by examining the contexts in which a page is cited. However, we find that it is not enough to determine separate domains of authority; our model additionally determines the local flow of authority based upon the relative similarity of the source and target authority domains. In this way, we differentiate both incoming and outgoing hyperlinks by topicality and importance rather than treating them indiscriminately. We find that this approach compares favorably to other topical ranking methods on two real-world datasets and produces an approximately 10% improvement in precision and quality of the top ten results over PageRank.