Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure

Sandryhaila, Aliaksei; Moura, J.M.F.

doi:10.1109/msp.2014.2329213

Cited by 699 publications

(453 citation statements)

References 37 publications

Supporting

Mentioning

448

Contrasting

Unclassified

Order By: Relevance

“…Large-scale data sets are collected and studied in numerous domains, from engineering sciences to social networks, commerce, biomolecular research, and security [1]. Particularly, digital data, generated from a variety of digital devices, are growing at astonishing rates.…”

Section: Introductionmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

641

361

View full text Add to dashboard Cite

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

show abstract

Section: Introductionmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

641

361

View full text Add to dashboard Cite

show abstract

“…. , n − 1) as per (12), and eigenvector u µ associated with λ µ be chosen as per (13). In addition, let the diagonalized matrix of S 0 by the orthogonal matrix P := (u 0 , u 1 , .…”

Section: Fundamental Equation Of Oscillation On Networkmentioning

confidence: 99%

“…One proposal that uses spectral graph theory for directed graphs transforms asymmetric Laplacian matrixes in Jordan canonical form via elementary transformation [11], [12]. However, since asymmetric Laplacian matrices do not have the same convenient properties that the symmetric Laplacian matrices have, decomposition of the dynamics into simple Fourier modes remains unavailable.…”

Section: Introductionmentioning

confidence: 99%

Oscillation Model for Describing Network Dynamics Caused by Asymmetric Node Interaction

Aida

Takano

Murata

2018

IEICE Trans. Commun.

115

View full text Add to dashboard Cite

SUMMARYThis paper proposes an oscillation model for analyzing the dynamics of activity propagation across social media networks. In order to analyze such dynamics, we generally need to model asymmetric interactions between nodes. In matrix-based network models, asymmetric interaction is frequently modeled by a directed graph expressed as an asymmetric matrix. Unfortunately, the dynamics of an asymmetric matrix-based model is difficult to analyze. This paper, first of all, discusses a symmetric matrix-based model that can describe some types of link asymmetry, and then proposes an oscillation model on networks. Next, the proposed oscillation model is generalized to arbitrary link asymmetry. We describe the outlines of four important research topics derived from the proposed oscillation model. First, we show that the oscillation energy of each node gives a generalized notion of node centrality. Second, we introduce a framework that uses resonance to estimate the natural frequency of networks. Natural frequency is important information for recognizing network structure. Third, by generalizing the oscillation model on directed networks, we create a dynamical model that can describe flaming on social media networks. Finally, we show the fundamental equation of oscillation on networks, which provides an important breakthrough for generalizing the spectral graph theory applicable to directed graphs.

show abstract

“…The workhorses of graph signal analysis are graph filters, which represent the building blocks for processing the spectral content of graph signals. Graph filters are useful to process, analyze networked data and solve wide range of problems and ideal for many tasks and applications [2], [3] such as distributed estimation.…”

Section: Introductionmentioning

confidence: 99%

Topology design to increase network lifetime in WSN for graph filtering in consensus processes

Saad

Weerasinghe

Beferull-Lozano

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-Graph filters, which are considered as the workhorses of graph signal analysis in the emerging field of signal processing on graphs, are useful for many applications such as distributed estimation in wireless sensor networks. Many of these tasks are based on basic distributed operators such as consensus, which are carried out by sensor devices under limited energy supply. To cope with the energy constraints, this paper focuses on designing the network topology in order to maximize the network lifetime and reduce the energy consumption when applying graph filters. The problem is a complex combinatorial problem and in this work, we propose two efficient heuristic algorithms for solving it. We show by simulations that they provide good performance in terms of the network lifetime and the total energy consumption of the filtering process.

show abstract

Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure

Cited by 699 publications

References 37 publications

A survey of machine learning for big data processing

A survey of machine learning for big data processing

Oscillation Model for Describing Network Dynamics Caused by Asymmetric Node Interaction

Topology design to increase network lifetime in WSN for graph filtering in consensus processes

Contact Info

Product

Resources

About