SALSA

By Ronny Lempel, S. Moran. 2001

411 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Statistical and Nonlinear Physics

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Citations in the corpus

Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, background): “Perhaps, the closest to our work here is the “who to follow” systems at Twitter [14, 15] that places the entire follow graph in memory of a single machine and runs a personalized SALSA algorithm [19].”
Structured Encryption and Controlled Disclosure (2010, methodology): “Several link-analysis algorithms for web search such as Kleinberg's seminal HITS algorithm [27] (and the related SALSA [28] algorithm) are local.”
Biased assimilation, homophily, and the dynamics of polarization (2012, methodology): “On the other hand, SimpleSALSA and SimpleICF, which first choose a random item liked by the user Author contributions: P.D., A.G., and D.T.L. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.”
Graph-based term weighting for information retrieval (2012, background): “More elaborate ranking algorithms have also been proposed that incorporate information about the node's content into the ranking, for instance anchor text (Chakrabarti et al. 1998), or that involve computationally lighter processes (Lempel and Moran 2001).”
Higher-order Web link analysis using multilinear algebra (2005, methodology): “For example, SALSA is a variant on HITS that uses a stochastic iteration matrix [27].”
Axioms for Centrality (2013, methodology): “How is it possible that PageRank and SALSA work so well (albeit less than harmonic centrality on the whole graph) with so little information?”
Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, neutral): “Lempel and Moran [131] propose SALSA, a stochastic algorithm for link structure analysis.”
WTF: the who to follow service at Twitter (2013, methodology): “A more refined approach would be to compute the top n nodes from personalized PageRank or the SALSA-based algorithm described in Section 5.2.”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, methodology): “, PageRank [82], which we’ll discuss in Chapter 5, or related metrics such as HITS [56] and SALSA [60]).”
GraphJet: Real-Time Content Recommendations at Twitter (2016, methodology): “The simplest form of this query is to begin with the vertex in the bipartite interaction graph that corresponds to the query user and run SALSA [23].”
Fast Incremental and Personalized PageRank (2010, methodology): “In this paper, we focus on (incremental) computation of PageRank [39], personalized PageRank [14,19,39], SALSA [30], and personalized SALSA [38, 40].”
A Survey on PageRank Computing (2005, methodology): “[Lempel and Moran, 2000], [Lempel and Moran, 2001] introduced the algorithm SALSA (Stochastic Approach for Link-Structure Analysis) that utilizes a random walk on two sides of a bi-partite graph, authorities (pages with non-trivial in-degree) and hubs (pages with non-trivial out-degree), associated…”
TripleRank: Ranking Semantic Web Data by Tensor Decomposition (2009, methodology): “We reconsider the paradigm of two-dimensional graph representation (e.g. adjacency matrix for HITS, probabilistic transition matrix for PageRank, a mixture of these models for SALSA, etc.) and represent semantic graphs by 3-dimensional tensors.”