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Federated Tobit Kalman Filtering Fusion With DeadZoneLike Censoring and Dynamical Bias Under the RoundRobin Protocol IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201215
Hang Geng; Zidong Wang; Fuad E. Alsaadi; Khalid H. Alharbi; Yuhua ChengThis paper is concerned with the multisensor filtering fusion problem subject to stochastic uncertainties under the RoundRobin protocol (RRP). The uncertainties originate from three sources, namely, censored observations, dynamical biases and additive white noises. To reflect the deadzonelike censoring phenomenon, the measurement observation is described by the Tobit model where the censored region

Weighted Average ConsensusBased Optimization of AdvectionDiffusion Systems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201215
Saber JafarizadehAs a fundamental algorithm for collaborative processing over multiagent systems, distributed consensus algorithm has been studied for optimizing its convergence rate. Due to the close analogy between the diffusion problem and the consensus algorithm, the previous trend in the literature is to transform the diffusion system from the spatially continuous domain into the spatially discrete one. In this

Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201222
Vitor R. M. Elias; Vinay Chakravarthi Gogineni; Wallace A. Martins; Stefan WernerThis paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters that operate on graphshifted versions of the input signal. We then propose a centralized graph kernel least mean squares (GKLMS) algorithm to identify nonlinear graph filters’ model parameters. To reduce

Recursive Secure Filtering Over GilbertElliott Channels in Sensor Networks: The Distributed Case IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201222
Derui Ding; Zidong Wang; QingLong Han; XianMing ZhangThis paper is concerned with the recursive secure filtering problem for a class of discretetime systems subject to unreliable communication due to the security vulnerability of sensor networks. The unreliable communication, caused probably by denialofservice cyberattacks, is described by the wellknown GilbertElliott model. The addressed nonlinearities are applicable for some of the most investigated

node2coords: Graph Representation Learning with Wasserstein Barycenters IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201202
Effrosyni Simou; Dorina Thanou; Pascal FrossardIn order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm

Multiscale Representation Learning of Graph Data With Node Affinity IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201215
Xing Gao; Wenrui Dai; Chenglin Li; Hongkai Xiong; Pascal FrossardGraph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neural networks. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose

List of Reviewers IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201222
Presents a list of reviewers who contributed to this publication in 2020.

A ComputationEfficient Decentralized Algorithm for Composite Constrained Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201116
Qingguo Lü; Xiaofeng Liao; Huaqing Li; Tingwen HuangThis paper focuses on solving the problem of composite constrained convex optimization with a sum of smooth convex functions and nonsmooth regularization terms ( ${\ell _1}$ norm) subject to locally general constraints. Motivated by the modern largescale information processing problems in machine learning (the samples of a training dataset are randomly decentralized across multiple computing nodes)

Joint Forecasting and Interpolation of TimeVarying Graph Signals Using Deep Learning IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201124
Gabriela Lewenfus; Wallace A. Martins; Symeon Chatzinotas; Björn OtterstenWe tackle the problem of forecasting networksignal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate timeseries forecasting (temporal prediction) and graphsignal interpolation (spatial prediction). This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our

BandwidthConstrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201116
Domenico Ciuonzo; S. Hamed Javadi; Abdolreza Mohammadi; Pierluigi Salvo RossiDecentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametricallyspecified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing

Convex Combination of Diffusion Strategies Over Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201116
Danqi Jin; Jie Chen; Cédric Richard; Jingdong Chen; Ali H. SayedCombining diffusion strategies with complementary properties enables enhanced performance when they can be run simultaneously. In this article, we first propose two schemes for the convex combination of two diffusion strategies, namely, the powernormalized scheme and the signregressor scheme. Then, we conduct theoretical analysis for one of the schemes, i.e., the powernormalized one. An analysis

Distributed State Estimation Under Random Parameters and Dynamic Quantizations Over Sensor Networks: A Dynamic EventBased Approach IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201119
Shaoying Wang; Zidong Wang; Hongli Dong; Yun ChenThis paper deals with the distributed state estimation problem for an array of discrete timevarying systems over sensor networks under dynamic eventbased transmission scheme (DETS), random parameter matrices (RPMs) and dynamic measurement quantization (DMQ). Different from the existing static eventbased transmission scheme with fixed threshold, the employed DETS introduces an auxiliary offset variable

Graph Learning for Spatiotemporal Signals With Long and ShortTerm Characterization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20201117
Yueliang Liu; Wenbin Guo; Kangyong You; Lei Zhao; Tao Peng; Wenbo WangMining natural associations from highdimensional spatiotemporal signals plays an important role in various fields including biology, climatology, and financial analysis. However, most existing works have mainly studied timeindependent signals without considering the correlations of spatiotemporal signals that achieve high learning accuracy. This paper aims to learn graphs that better reflect underlying

MultiSlot Distributed Measurement Selection: A Sparsity Learning Approach IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200911
Qian Xia; Wei Wang; Rong Ran; Yi Gong; Zhaoyang ZhangWhen only a part of measurements are collected sequentially in largescale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation

Massively Distributed Graph Distances IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200907
Armin Moharrer; Jasmin Gao; Shikun Wang; José Bento; Stratis IoannidisGraph distance (or similarity) scores are used in several graph mining tasks, including anomaly detection, nearest neighbor and similarity search, pattern recognition, transfer learning, and clustering. Graph distances that are metrics and, in particular, satisfy the triangle inequality, have theoretical and empirical advantages. Wellknown graph distances that are metrics include the chemical or the

Ambient Noise Tomography With Common Receiver Clusters in Distributed Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200825
Sili Wang; Fangyu Li; Mark Panning; Saikiran Tharimena; Steve Vance; WenZhan SongNearsurface imaging with distributed sensor networks (DSN) is promising for planet exploration, which affordably generates a nearsurface velocity model. Recently, an Eikonal tomographybased ambient noise seismic imaging (ANSI) algorithm was implemented in a DSN to realize realtime and insitu nearsurface imaging. However, only using data from neighbors to generate a velocity map cannot have enough

FastandSecure StateEstimation in DynamicControl Over Communication Channels: A GameTheoretical Viewpoint IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200820
Makan Zamanipour“Control over noisy communicationchannels” invented by SahaiMitterandTatikonda is a prominent topic. In this context, the latencyandreliability tradeoff is considered by responding to the following: How much fast? How much secure? For a stochasticmeanfieldgame (SMFG), we assign the sourcecodes as the agents. Additionally, the totalReward is the Volume of the maximum secure lossy sourcecodingrate

On Inference of Network Topology and Confirmation Bias in CyberSocial Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200907
Yanbing Mao; Emrah AkyolThis article studies topology inference, from agent states, of a directed cybersocial network with opinion spreading dynamics model that explicitly takes confirmation bias into account. The cybersocial network comprises a set of partially connected directed network of agents at the social level, and a set of information sources at the cyber layer. The necessary and sufficient conditions for the existence

Sensor Selection and Design for Binary Hypothesis Testing in the Presence of a Cost Constraint IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200813
Berkay Oymak; Berkan Dulek; Sinan GeziciWe consider a sensor selection problem for binary hypothesis testing with costconstrained measurements. Random outputs related to a parameter vector of interest are assumed to be generated by a linear system corrupted with Gaussian noise. The aim is to decide on the state of the parameter vector based on a set of measurements collected by a limited number of sensors. The cost of each sensor measurement

Gaussian Mixture Particle JumpMarkovCPHD Fusion for Multitarget Tracking Using Sensors With Limited Views IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200813
Kai Da; Tiancheng Li; Yongfeng Zhu; Qiang FuIn this article, we propose a multisensor cardinalized probability density hypothesis (CPHD) filter for tracking an unknown number of targets that may maneuver over time by using a sensor network with partially overlapping fields of views (POFoVs). We develop a novel, Gaussian mixture particle (GMP) implementation of the jumpMarkov CPHD filter to deal with highly nonlinear/nonGaussian models and

Extended Adjacency and ScaleDependent Graph Fourier Transform via Diffusion Distances IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200811
Vitor R. M. Elias; Wallace A. Martins; Stefan WernerThis article proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency matrix. In the proposed model, additional edges are created according to a Markov relation imposed between nodes. This information is incorporated into the extendedadjacency matrix as a function of

Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200728
Rex C. Y. Cheung; Alexander Aue; Seungyong Hwang; Thomas C. M. LeeIn many complex systems, networks and graphs arise in a natural manner. Often, time evolving behavior can be easily found and modeled using timeseries methodology. Amongst others, two common research problems in network analysis are community detection and changepoint detection. Community detection aims at finding specific substructures within the networks, and changepoint detection tries to find

Lower Bound Accuracy of BearingBased Localization for Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200731
Wenjing Zhong; Xiaoyuan Luo; Xiaolei Li; Jing Yan; Xinping GuanLocalization accuracy is an important indicator to design, and deploy the wireless sensor network. This article quantitatively investigates the bearingbased localization accuracy (BBLA) from the perspective of network geometric structure. The average geometric dilution of precision (AGDOP) is used to model the geometric structurebased BBLA, and is expressed by the geometry matrix which is based on

Interval Observer Design Under Stealthy Attacks and Improved EventTriggered Protocols IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200729
Xin Li; Guoliang Wei; Derui DingIn this article, an interval estimation problem is investigated for a class of discretetime nonlinear networked systems under stealthy attacks. An improved eventtriggered protocol with the timevarying threshold is adopted to govern the received signals of interval observer so as to reduce unnecessary data communication burden. Stealthy attacks occurring in both the sensors and the plant come typically

Recovery of TimeVarying Graph Signals via Distributed Algorithms on Regularized Problems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200722
Junzheng Jiang; David B. Tay; Qiyu Sun; Shan OuyangThe recovery of missing samples from available noisy measurements is a fundamental problem in signal processing. This process is also sometimes known as graph signal inpainting, reconstruction, forecasting or inference. Many of the existing algorithms do not scale well with the size of the graph and/or they cannot be implemented efficiently in a distributed manner. In this paper, we develop efficient

Bearing RigidityBased Localizability Analysis for Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200724
Xiaoyuan Luo; Wenjing Zhong; Xiaolei Li; Xinping GuanThe localizability analysis for wireless sensor network is of great significance to network localization, and topology control. In this paper, the localizability problem for the bearingbased localization is investigated. An identification method for bearing rigid component is presented, and the localizability is studied for the determined bearing rigid component. In the identification process for

TACC: TopologyAware Coded Computing for Distributed Graph Processing IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200528
Başak Güler; A. Salman Avestimehr; Antonio OrtegaThis article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in largescale distributed graph processing. In particular, we propose a topologyaware coded computing (TACC) algorithm that has two novel salient features: (i) a topologyaware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for

Exploiting the Agent's Memory in Asymptotic and FiniteTime Consensus Over MultiAgent Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200615
Gianni Pasolini; Davide Dardari; Michel KiefferThis article proposes two average consensus algorithms exploiting the memory of agents. The performance of the proposed as well as of several stateoftheart consensus algorithms is evaluated considering different communication ranges, and evaluating the impact of transmission errors. To compare asymptotic and finitetime average consensus schemes, the $\varepsilon$ convergence time is adopted for

Scalable Data Association for Extended Object Tracking IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200521
Florian Meyer; Moe Z. WinTracking extended objects based on measurements provided by light detection and ranging (LIDAR) and millimeter wave radio detection and ranging (RADAR) sensors is a key task to obtain situational awareness in important applications including autonomous driving and indoor robotics. In this paper, we propose probabilistic data association methods for localizing and tracking of extended objects that originate

On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200518
Mojtaba Shirazi; Azadeh VosoughiWe consider distributed estimation of a random source in a hierarchical power constrained wireless sensor network. Sensors within each cluster send their measurements to a cluster head (CH). CHs optimally fuse the received signals and transmit to the fusion center (FC) over orthogonal fading channels. To enable channel estimation at the FC, CHs send pilots, prior to data transmission. We derive the

Precoder Feedback Schemes for Robust Interference Alignment With Bounded CSI Uncertainty IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200520
Navneet Garg; Aditya K. Jagannatham; Govind Sharma; Tharmalingam RatnarajahThis article presents limited feedbackbased precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the minmax mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization. The proposed precoder and decoder designs capture

Collaborative MultiSensing in Energy Harvesting Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200518
Vini Gupta; Swades DeThis article presents an adaptive multisensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple crosscorrelated slowlyvarying parameters/signals. Inherent spatiotemporal correlations of the observed parameters are exploited to adaptively activate a subset of sensors of a few nodes and turnOFF

Mask Combination of MultiLayer Graphs for Global Structure Inference IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200518
Eda Bayram; Dorina Thanou; Elif Vural; Pascal FrossardStructure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although realworld data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well

Energy Efficient Spectrum Allocation and Mode Selection for D2D Communications in Heterogeneous Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200511
Apostolos Galanopoulos; Fotis Foukalas; Tamer KhattabIn this paper, we consider a heterogeneous network consisting of both macro Base Station (MBS) and pico Base Stations (PBSs) in order to provide a spectrum allocation and mode selection in devicetodevice (D2D) communications. A number of Component Carriers (CC) are considered available for allocation to the MBS and PBSs that are being utilized through carrier aggregation (CA) while mode selection

Bayesian Inference of Network Structure From Information Cascades IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200427
Caitlin Gray; Lewis Mitchell; Matthew RoughanContagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures

IEEE Transactions on Signal and Information Processing over Networks publication information IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Resilient Distributed Diffusion in Networks With Adversaries IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191206
Jiani Li; Waseem Abbas; Xenofon KoutsoukosIn this article, we study resilient distributed diffusion for multitask estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusionbased information processing rules. In particular, by exploiting

CommunicationCensored Linearized ADMM for Decentralized Consensus Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191206
Weiyu Li; Yaohua Liu; Zhi Tian; Qing LingIn this paper, we propose a communication and computationefficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization

Optimized Transmission for Parameter Estimation in Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191004
Shahin Khobahi; Mojtaba Soltanalian; Feng Jiang; A. Lee SwindlehurstA central problem in analog wireless sensor networks is to design the gain or phaseshifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an overparametrization of the original design problem

VectorValued Graph Trend Filtering With NonConvex Penalties IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191206
Rohan Varma; Harlin Lee; Jelena Kovačević; Yuejie ChiThis article studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vectorvalued. We extend the graph trend filtering framework to denoising vectorvalued graph signals with a family of nonconvex regularizers, which exhibit superior recovery performance over existing convex regularizers. Using an oracle

Online Distributed Learning Over Graphs With Multitask GraphFilter Models IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Fei Hua; Roula Nassif; Cédric Richard; Haiyan Wang; Ali H. SayedIn this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular graphshift operators such as those based on the graph Laplacian matrix, or the adjacency matrix, are not energy preserving. This may result in an illconditioned

Minimal Sufficient Conditions for Structural Observability/Controllability of Composite Networks via Kronecker Product IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191216
Mohammadreza Doostmohammadian; Usman A. KhanIn this article, we consider composite networks formed from the Kronecker product of smaller networks. We find the observability and controllability properties of the product network from those of its constituent smaller networks. The overall network is modeled as a LinearStructureInvariant (LSI) dynamical system where the underlying matrices have a fixed zero/nonzero structure but the nonzero

Fastest Mixing Reversible Markov Chain: Clique Lifted Graphs and Subgraphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Saber JafarizadehMarkov chains are one of the wellknown tools for modeling and analyzing stochastic systems. At the same time, they are used for constructing random walks that can achieve a given stationary distribution. This paper is concerned with determining the transition probabilities that optimize the mixing time of the reversible Markov chains towards a given equilibrium distribution. This problem is referred

Efficient Graph Learning From Noisy and Incomplete Data IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Peter Berger; Gabor Hannak; Gerald MatzWe consider the problem of learning a graph from a given set of smooth graph signals. Our graph learning approach is formulated as a constrained quadratic program in the edge weights. We provide an implicit characterization of the optimal solution and propose a tailored ADMM algorithm to solve this problem efficiently. Several nearest neighbor and smoothness based graph learning methods are shown to

NodeCentric Graph Learning From Data for Brain State Identification IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Nafiseh Ghoroghchian; David M. Groppe; Roman Genov; Taufik A. Valiante; Stark C. DraperDatadriven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph node. Existing graph learning methods either use simplified models for the graph signal, or they are prohibitively expensive in terms of computational and memory requirements. This is particularly true when the number of

Recovering the Structural Observability of Composite Networks via Cartesian Product IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200116
Mohammadreza DoostmohammadianObservability is a fundamental concept in system inference and estimation. This article is focused on structural observability analysis of Cartesian product networks. Cartesian product networks emerge in variety of applications including in parallel and distributed systems. We provide a structural approach to extend the structural observability of the constituent networks (referred as the factor networks)

ComputationEfficient Distributed Algorithm for Convex Optimization Over TimeVarying Networks With Limited Bandwidth Communication IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200116
Huaqing Li; Chicheng Huang; Zheng Wang; Guo Chen; Hafiz Gulfam Ahmad UmarA novel computationefficient quantized distributed optimization algorithm is presented in this article for solving a class of convex optimization problems over timevarying undirected networks with limited communication capacity. These convex optimization problems are usually relevant to the minimization of a sum of local convex objective functions using only local communication and local computation

OrthoNet: Multilayer Network Data Clustering IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200129
Mireille El Gheche; Giovanni Chierchia; Pascal FrossardNetwork data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some information about the network edges. In some cases, this network information can even be given with multiple views or layers, each one representing

Learning Graphs From Linear Measurements: Fundamental TradeOffs and Applications IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200224
Tongxin Li; Lucien Werner; Steven H. LowWe consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain highdegree nodes), based on which we study fundamental tradeoffs between the number of measurements, the complexity of the graph class, and the probability

Distributed Learning Algorithms for Optimal Data Routing in IoT Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200221
Michele Rossi; Marco Centenaro; Aly Ba; Salma Eleuch; Tomaso Erseghe; Michele ZorziWe consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a sourcespecific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multihop fashion until it reaches a data collector (the IoT gateway)

Distributed Discrete Hashing by Equivalent Continuous Formulation IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200220
Shengnan Wang; Chunguang Li; HuiLiang ShenHashing based approximate nearest neighbor search has attracted considerable attention in various fields. Most of the existing hashing methods are centralized, which cannot be used for many largescale applications with the data stored or collected in a distributed manner. In this article, we consider the distributed hashing problem. The main difficulty of hashing is brought by its inherent binary

StateSpace Network Topology Identification From Partial Observations IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200220
Mario Coutino; Elvin Isufi; Takanori Maehara; Geert LeusIn this article, we explore the statespace formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified view of network control and signal processing on graphs

Spectral Graph Based VertexFrequency Wiener Filtering for Image and Graph Signal Denoising IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200227
Ali Can Yağan; Mehmet Tankut ÖzgenIn this article, we propose and develop a spectral graph based vertex varying Wiener filtering framework in the joint vertexfrequency domain for denoising of graph signals defined on weighted, undirected and connected graphs. To this end, we first extend the Zadeh timefrequency filter concept to graph signals and obtain vertexfrequency transfer function of the proposed Wiener filter by transforming

Distributed Sensor Networks Based Shallow Subsurface Imaging and Infrastructure Monitoring IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200220
Fangyu Li; Maria Valero; Yifang Cheng; Tong Bai; WenZhan SongDistributed sensor networks can be used as passive seismic sensors to image and monitor subsurface and underground activities. Passive seismic surfacewave imaging adopts background ambient sounds from a farfield energy source. Because high frequency components decay a lot between the neighboring stations, conventional sparse sensor networks cannot image smallscale and shallow objects. In this article

Incremental Coding for Extractable Compression in the Context of Massive Random Access IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200318
Thomas Maugey; Aline Roumy; Elsa Dupraz; Michel KiefferIn this paper, we study the problem of source coding with Massive Random Access (MRA). A set of correlated sources is encoded once for all and stored on a server while a large number of clients access various subsets of these sources. Due to the number of concurrent requests, the server is only able to extract a bitstream from the stored data: no reencoding can be performed before the transmission

Graph Laplacian Mixture Model IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200330
Hermina Petric Maretic; Pascal FrossardGraph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph. This is, however, not always the case, as data is often available in mixed form, yielding the need for methods

ClusteringAware Graph Construction: A Joint Learning Perspective IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200420
Yuheng Jia; Hui Liu; Junhui Hou; Sam KwongGraphbased clustering methods have demonstrated the effectiveness in various applications. Generally, existing graphbased clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering

Edge Caching in MultiUAVEnabled Radio Access Networks: 3D Modeling and Spectral Efficiency Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200413
Fasheng Zhou; Ning Wang; Gaoyong Luo; Lisheng Fan; Wei ChenUnmannedaerialvehicle (UAV) enabled radio access is an effective technology to improve the wireless coverage, in particular for remote and disasterstruck areas. It will become a key enabler in the forthcoming 5G heterogeneous cellular networks to provide improved and resilient coverage. In this article, we study edge caching for multiple UAVenabled radio access networks (UAVRANs) and investigate

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200403
Gaëtan Frusque; Julien Jung; Pierre Borgnat; Paulo GonçalvesFunctional connectivity (FC) is a graphlike data structure commonly used by neuroscientists to study the dynamic behaviour of brain activity. However, these analyses rapidly become complex and timeconsuming, since the number of connectivity components to be studied is quadratic with the number of electrodes. In this work, we address the problem of clustering FC into relevant ensembles of simultaneously

Network Inference From Consensus Dynamics With Unknown Parameters IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200402
Yu Zhu; Michael T. Schaub; Ali Jadbabaie; Santiago SegarraWe explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discretetime consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics