Modeling and Multiway Analysis of Chatroom Tensors

Acar, Evrim; Camtepe, Seyit; Krishnamoorthy, Mukkai S.; Yener, Bülent

doi:10.1007/11427995_21

Cited by 99 publications

(100 citation statements)

References 14 publications

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“…Sun et al [38] apply a three-way Tucker decomposition [40] to the analysis of (user × query term × web page) data in order to personalize web search. In [1], various tensor decompositions of (user × key word × time) data are used to separate different streams of conversation in chatroom data. In [26,25] a PARAFAC decomposition [11] (also known as the Canonical Decomposition or CANDECOMP decomposition [4]) on a (web page × web page × anchor text) was used on a sparse, three-way tensor representing the web graph with anchor-textlabeled edges.…”

Section: Dedicom and Multi-way Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Temporal analysis of social networks using three-way DEDICOM.

Bader¹,

Harshman²,

Kolda

2006

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DEDICOM is an algebraic model for analyzing intrinsically asymmetric relationships, such as the balance of trade among nations or the flow of information among organizations or individuals. It provides information on latent components in the data that can be regarded as "properties" or "aspects" of the objects, and it finds a few patterns that can be combined to describe many relationships among these components. When we apply this technique to adjacency matrices arising from directed graphs, we obtain a smaller graph that gives an idealized description of its patterns. Three-way DEDICOM is a higher-order extension of the model that has certain uniqueness properties. It allows for a third mode of the data, such as time, and permits the analysis of semantic graphs. We present an improved algorithm for computing three-way DEDICOM on sparse data and demonstrate it by applying it to the adjacency tensor of a semantic graph with time-labeled edges. Our application uses the Enron email corpus, from which we construct a semantic graph corresponding to email exchanges among Enron personnel over a series of 44 months. Meaningful patterns are recovered in which the representation of asymmetries adds insight into the social networks at Enron.

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Section: Dedicom and Multi-way Modelsmentioning

confidence: 99%

“…In the 2-way case, a square matrix X is decomposed according to (1). The goal is to find the best-fitting matrices A and R such that E F is minimized.…”

Section: Two-way Dedicommentioning

confidence: 99%

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Temporal analysis of social networks using three-way DEDICOM.

Bader¹,

Harshman²,

Kolda

2006

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“…Tensor factorizations or decompositions play a fundamental role in enhancing the data and extracting latent components. In the era, tensor is used in a wide variety of applications such as in signal processing [5], data mining [6], neuroscience [7], and many more. In various signal processing applications, instrumental data contains information in more than two dimensions.…”

Section: Introductionmentioning

confidence: 99%

A Survey on PARATREE and SUSVD Decomposition Techniques and Their Use in Array Signal Processing

Bhatt¹,

Kumar²

2013

IJIGSP

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-The present manuscript is intended to review few applications of tensor decomposition model in array signal processing. Tensor decomposition models like HOSVD, SVD and PARAFAC are useful in signal processing. In this paper we shall use higher order tensor decomposition in signal processing. Also, a novel orthogonal non-iterative tensor decomposition technique (SUSVD), which is scalable to arbitrary high dimensional tensor, has been applied in MIMO channel estimation. The SUSVD provides a tensor model with hierarchical tree structure between the factors in different dimensions. We shall use a new model known as PARATREE, which is related to PARAFAC tensor models. The PARAFAC and PARATREE both describe a tensor as a sum of rank-1 tensors, but PARATREE has several advantages over PARAFAC, when it is applied as a lower rank approximation technique. PARATREE is orthogonal, fast and reliable to compute, and the order of the decomposition can be adaptively adjusted. The low rank PARATREE approximation has been applied to measure noise suppression for tensor valued MIMO channel sounding measurements.

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“…For more details on these methods the reader is referred to [31]. Also, the studies in [2] and [1] provide very detailed comparison between factorization methods.…”

mentioning

confidence: 99%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta

Özcan

2015

Information Sciences

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Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

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Modeling and Multiway Analysis of Chatroom Tensors

Cited by 99 publications

References 14 publications

Temporal analysis of social networks using three-way DEDICOM.

Temporal analysis of social networks using three-way DEDICOM.

A Survey on PARATREE and SUSVD Decomposition Techniques and Their Use in Array Signal Processing

A tensor-based selection hyper-heuristic for cross-domain heuristic search

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