Higher-order Web link analysis using multilinear algebra

By Tamara G. Kolda, Brett W. Bader, Joseph P. Kenny. Industrial Conference on Data Mining, 2005

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/16dace2a89d39676515dfaea18ca82b90e884200

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In Artificial Intelligence

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In Information Systems

Citations in the corpus

Multiplex Interbank Networks and Systemic Importance: An Application to European Data (2016, unknown): “See Kolda et al. (2005) for a proposal on network centrality using tensor decompositions and Bonacina et al. (2015) for an application to networks of corporate governance in italian firms.”
Multilinear operators for higher-order decompositions (2006, background): “Higher-order tensor decompositions are in frequent use today in a variety of fields including psychometrics [43, 12, 19], chemometrics [6, 37], image analysis [45, 36, 46], graph analysis [27, 26], signal processing [13, 33], and much more [1, 38, 32].”
Tensors for Data Mining and Data Fusion (2016, methodology): “Kolda et al. [2005] extend Kleinberg’s HITS algorithm for authoritativeness and hub-ness scores of webpages, including context information on the link.”
Krylov-Type Methods for Tensor Computations (2010, unknown): “, [19] and several references in the survey [20].”
Krylov-type methods for tensor computations I (2013, background): “, [19] and several references in the survey [20].”
Tensor Decomposition for Signal Processing and Machine Learning (2016, background): “Signal processing followed, in the 90’s, but the real spark that popularized tensors came when the computer science community (notably those in machine learning, data mining, computing) discovered the power of tensor decompositions, roughly a decade ago [1]–[3].”
The structure and dynamics of multilayer networks (2014, methodology): “These methods have been used to extract communities [104], to rank nodes [109,110] and to perform data mining [103,111,112].”
Scalable Tensor Factorizations for Incomplete Data (2010, background): “We focus here on the CANDECOMP/PARAFAC (CP) tensor decomposition [14, 15], which is a tensor model commonly used in various applications [6, 16, 17, 18, 19].”
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018, methodology): “The techniques on tensors have thus been attracting much attention recently and helped enhance performance of various practical tasks, such as MSI denoising [69], 3D image reconstruction [59], and higher-order web link analysis [32].”
Beyond streams and graphs: dynamic tensor analysis (2006, methodology): “We are pleased to acknowledge Brett Bader and Tamara Kolda from Sandia National lab for providing the sparse tensor package [ 20 ].”
Scalable Tensor Decompositions for Multi-aspect Data Mining (2008, background): “These examples can be naturally represented as tensors as follows: (a) can be modeled as a 4D array, or a fourth-order tensor, with sender, recipeient, keyword, and datestamp as the four modes; (b) can viewed as a author-conference-keyword thirdorder tensor [19]; (c) hyperlinks on the web yield a th”
TripleRank: Ranking Semantic Web Data by Tensor Decomposition (2009, methodology): “The solution presented in [26] uses for Web authority ranking the higher-order representation of the hyperlink graph by labeling the graph edges with the anchor text of the hyperlinks.”
Unsupervised Multiway Data Analysis: A Literature Survey (2009, methodology): “As a generalization of incrementally computing the rankone decomposition of a matrix, a greedy algorithm has also been given in [60] to fit an R-component PARAFAC model.”
Factorization strategies for third-order tensors (2011, background): “Applications involving operations with tensors include chemometrics [41], psychometrics [24], signal processing [9] [27] [39], computer vision [44] [46] [45], data mining [1] [38], graph analysis [21], neuroscience [3] [30] [31], and many more.”
Temporal Link Prediction Using Matrix and Tensor Factorizations (2010, methodology): “nd Xiong et al. [2010] propose a probabilistic tensor factorization to address the problem of collaborative ltering over time. Tensor factorizations have been previously applied in web link analysis [Kolda et al. 2005] and also in social networks for the analysis of chatroom [Acar et al. 2006] and e”
Multilayer networks (2013, methodology): “Similar to using the SVD of an adjacency matrix to find communities in a monoplex network, one can employ tensor-decomposition methods [186] to detect communities in a multiplex network [52, 114, 187].”