The stochastic approach for link-structure analysis (SALSA) and the TKC effect

By Ronny Lempel, S. Moran. Comput. Networks, 2000

586 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9f44c93cadcd65d15ba4990d6bc2ae1fa25b3283

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In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Predicting the popularity of web 2.0 items based on user comments (2014, background): “There are other graph ranking algorithms that are specifically designed for bipartite graphs which are more relevant, such as HITS [19], SALSA [21], Co-HITS [9] and CoRank [36].”
Measuring user influence on Twitter: A survey (2015, methodology): “It is a combination of the Tweet count score activity measure (see Section 5) and the User inﬂuence score (UI) , which is a mechanism based on PageRank that can also use other algorithms such as HITS or SALSA [77].”
Using ODP metadata to personalize search (2005, methodology): “There are several algorithms for calculating this measure, the most common ones being HITS [13] and its more stable improvements SALSA [15] and Randomized HITS [19].”
Deeper Inside PageRank (2004, methodology): “A ﬁnal related algorithm is the SALSA method of Lempel and Moran [81], which uses a bipartite graph of the Web to create two Markov chains for ranking pages.”
Vital nodes identification in complex networks (2016, methodology): “The matrix form of HITs algorithm and its further discussion can be found in Refs.”
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…”
Co-authorship networks in the digital library research community (2005, methodology): “, 1998), SALSA (Lempel and Moran, 2000) and HITS (Kleinberg, 1999).”
Link analysis ranking: algorithms, theory, and experiments (2005, methodology): “…jordan, moon landing, movies, national parks, net censorship, randomized algorithms, recipes, roswell, search engines, shakespeare, table tennis, weather, vintage cars Many of these queries have already appeared in previous works [Dwork et al. 2001; Lempel and Moran 2000; Kleinberg 1998].”
Algorithms for estimating relative importance in networks (2003, methodology): “Lempel and Moran (2000) described av ariation of HITS called SALSA that can be understood as a random walk on a bipartite graph of hubs.”
Understanding and combating link farming in the twitter social network (2012, unknown): “Lempel et al. [21] showed that pages within a tightly-knit community (TKC) get high scores in the HITS algorithm (known as the ‘TKC eﬀect’).”
A Survey of Eigenvector Methods for Web Information Retrieval (2005, methodology): “SALSA, developed by Lempel and Moran in 2000, was spawned by the combination of ideas from both HITS and PageRank [42].”
Diffusion maps (2006, methodology): “Several variations around this theme appear in the literature [7,8], where, just like PageRank, various techniques provide a ranking function based on the top eigenvector of a Markov matrix [9,10].”
Link mining: a survey (2005, background): “This approach bears a relation to PageRank with two separate random walks—one with hub transitions and one with authority transitions—on a corresponding bipartite graph of hubs and authorities [73; 95; 84].”