Stable algorithms for link analysis

By Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e72ec7b09b7802dc2cfff328ba0602948734ea88

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Citations in the corpus

Link analysis ranking: algorithms, theory, and experiments (2005, methodology): “The seminal work of Kleinberg [1998] and Brin and Page [1998] was followed by an avalanche of Link Analysis Ranking algorithms (hereinafter denoted LAR algorithms) [Borodin et al. 2001; Bharat and Henzinger 1998; Lempel and Moran 2000; Rafiei and Mendelzon 2000; Azar et al. 2001; Achlioptas et al. 2”
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].”
Fast personalized PageRank on MapReduce (2011, methodology): “Our algorithms can also be applied to other personalized random walk based measures, such as personalized SALSA [21, 24].”
Inside PageRank (2005, methodology): “In order to explain the computational properties of the algorithm, most of the authors [Ng et al. 2001b; Zhang and Dong 2000; Brin et al. 1999] cite the general theory of Markov chains [Motwani and Raghavan 1995; Seneta 1981].”
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].”
A Time-dependent SIR model for COVID-19 (2020, unknown): “在流行病的典型數學表示中,我們會將疾病的動態改變簡化為幾種基本的疾病狀態。其中一個簡單的版本便是將疾病的狀態分成三個狀態的 Susceptible-infectedrecovered (SIR) model [11],三個狀態分別是 susceptible、 infected 和 recovered。我們將一個在 t 時間尚未得病, 但若接觸感染者便有可能得病的狀態稱作 susceptible; infected 態是指在 t 時間一個患有疾病,且具傳染能力的個體,若與 susceptible態的個體接觸,有一定的可能將其傳染的狀態;recovered態則是在 t時間從 infecte”
Higher-order Web link analysis using multilinear algebra (2005, background): “[30, 31] have analyzed the stability of PageRank and HITS.”
Deeper Inside PageRank (2004, methodology): “The experiments done by the Berkeley group involve removing a random 30% of their dataset and recomputing the importance vector [95].”
Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, unknown): “[158] shows that HITS’s long-range reinforcement is bad for stability : random erasure of a small fraction (say 10%) of nodes or edges can seriously alter the ranks of hubs and authorities.”
Vital nodes identification in complex networks (2016, methodology): “[131] studied the stability of ranking under small perturbations using HITs algorithm.”
Mining significant semantic locations from GPS data (2010, methodology): “85, thus following the PageRank [18] and the randomized HITS [16] algorithms.”
The Importance of Prior Probabilities for Entry Page Search (2002, unknown): “HITS and PageRank (or variants of those) are reported to be successful in numerous studies of retrieval quality [18, 3, 1, 25, 5], however when measuring relevance only, without taking quality or authoritativeness into account, HITS and PageRank based methods have not been able to improve retrieval…”
Link mining: a survey (2005, methodology): “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].”
A Survey of Eigenvector Methods for Web Information Retrieval (2005, methodology): “These methods include PHITS [19], SALSA [41], pSALSA [13], exponentiated HITS [26], and Randomized HITS [49].”