Stochastic Approach for Link-Structure Analysis (SALSA) is a web page ranking algorithm designed by R. Lempel and S. Moran to assign high scores to hub and authority web pages based on the quantity of hyperlinks among them.
SALSA is inspired by two other link-based ranking algorithms, namely HITS and PageRank, in the following ways:
SALSA can be seen as an improvement of HITS.
It is computationally lighter since its ranking is equivalent to a weighted in/out degree ranking. The computational cost of the algorithm is a crucial factor since HITS and SALSA are computed at query time and can therefore significantly affect the response time of a search engine. This should be contrasted with query-independent algorithms like PageRank that can be computed off-line.
SALSA is less vulnerable to the Tightly Knit Community (TKC) effect than HITS. A TKC is a topological structure within the Web that consists of a small set of highly interconnected pages. The presence of TKCs in a focused subgraph is known to negatively affect the detection of meaningful authorities by HITS.
The Twitter Social network uses a SALSA style algorithm to suggest accounts to follow.