Efficient and effective link analysis with precomputed salsa maps

SALSA is a link-based ranking algorithm that takes the result set of a query as input, extends the set to include additional neighboring documents in the web graph, and performs a random walk on the induced subgraph. The stationary probability distribution of this random walk, used as a relevance score, is significantly more effective for ranking purposes than popular query-independent link-based ranking algorithms such as PageRank. Unfortunately, this requires significant effort at query-time, to access the link graph and compute the stationary probability distribution. In this paper, we explore whether it is possible to perform most of the computation off-line, prior to the arrival of any queries. The off-line phase of our approach computes a "score map" for each node in the web graph by performing a SALSA-like algorithm on the neighborhood of that node and retaining the scores of the most promising nodes in the neighborhood graph. The on-line phase takes the results to a ...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Marc Najork, Nick Craswell
• Keywords: CIKM 2008, Information Management, Link-based Ranking, Link-based Ranking Algorithms, Stationary Probability Distribution