Mining neighbors' topicality to better control authority flow

Web pages are often recognized by others through contexts. These contexts determine how linked pages influence and interact with each other. When differentiating such interactions, the authority of web pages can be better estimated by controlling the authority flows among pages. In this work, we determine the authority distribution by examining the topicality relationship between associated pages. In addition, we find it is not enough to quantify the influence of authority propagation from only one type of neighbor, such as parent pages in PageRank algorithm, since web pages, like people, are influenced by diverse types of neighbors within the same network. We propose a probabilistic method to model authority flows from different sources of neighbor pages. In this way, we distinguish page authority interaction by incorporating the topical context and the relationship between associated pages. Experiments on the 2003 and 2004 TREC Web Tracks demonstrate that this approach outperforms other competitive topical ranking models and produces a more than 10% improvement over PageRank on the quality of top 10 search results. When increasing the types of incorporated neighbor sources, the performance shows stable improvements.