IEEE Transactions on Signal and Information Processing over Networks最新文献_第3页

Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals 有限时间顶点图信号的子集随机采样与重构

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-11 DOI: 10.1109/TSIPN.2025.3597466

Hang Sheng;Qinji Shu;Hui Feng;Bo Hu

{"title":"Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals","authors":"Hang Sheng;Qinji Shu;Hui Feng;Bo Hu","doi":"10.1109/TSIPN.2025.3597466","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3597466","url":null,"abstract":"Finite time-vertex graph signals (FTVGS) provide an efficient representation for capturing spatio-temporal correlations across multiple data sources on irregular structures. Although sampling and reconstruction of FTVGS with known spectral support have been extensively studied, the case of unknown spectral support requires further investigation. Existing random sampling methods may extract samples from any vertex at any time, but such strategies are not friendly in practice, where sampling is typically limited to a subset of vertices and moments. To address this requirement, we propose a subset random sampling scheme for FTVGS. Specifically, we first randomly select a subset of rows and columns to form a submatrix, followed by random sampling within that submatrix. In theory, we provide sufficient conditions for reconstructing the original FTVGS with high probability. Additionally, we introduce a reconstruction framework incorporating low-rank, sparsity, and smoothness priors (LSSP), and verify the feasibility of the reconstruction and the effectiveness of the framework through experiments.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1015-1029"},"PeriodicalIF":3.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Graph Topology Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation 基于时间顶点自适应滤波器的在线图拓扑学习：从理论到心脏纤颤

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-06 DOI: 10.1109/TSIPN.2025.3594003

Alexander Jenkins;Thiernithi Variddhisai;Ahmed El-Medany;Fu Siong Ng;Danilo Mandic

{"title":"Online Graph Topology Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation","authors":"Alexander Jenkins;Thiernithi Variddhisai;Ahmed El-Medany;Fu Siong Ng;Danilo Mandic","doi":"10.1109/TSIPN.2025.3594003","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3594003","url":null,"abstract":"Graph Signal Processing (GSP) provides a powerful framework for analysing complex, interconnected systems by modelling data as signals on graphs. While recent advances have enabled graph topology learning from observed signals, existing methods often struggle with time-varying systems and real-time applications. To address this gap, we introduce AdaCGP, a sparsity-aware adaptive algorithm for dynamic graph topology estimation from multivariate time series. AdaCGP estimates the Graph Shift Operator (GSO) through recursive update formulae designed to address sparsity, shift-invariance, and bias. Through comprehensive simulations, we demonstrate that AdaCGP consistently outperforms multiple baselines across diverse graph topologies, achieving improvements exceeding 83% in GSO estimation compared to state-of-the-art methods while maintaining favourable computational scaling properties. Our variable splitting approach enables reliable identification of causal connections with near-zero false alarm rates and minimal missed edges. Applied to cardiac fibrillation recordings, AdaCGP tracks dynamic changes in propagation patterns more effectively than established methods like Granger causality, capturing temporal variations in graph topology that static approaches miss. The algorithm successfully identifies stability characteristics in conduction patterns that may maintain arrhythmias, demonstrating potential for clinical applications in diagnosis and treatment of complex biomedical systems.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"965-979"},"PeriodicalIF":3.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geometric Methods for Resilient Aggregation and Safe Point Computation in Adversarial Multiagent Networks With Imprecise Data 数据不精确的对抗多智能体网络中弹性聚合和安全点计算的几何方法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-05 DOI: 10.1109/TSIPN.2025.3594015

Christopher A. Lee;Waseem Abbas

{"title":"Geometric Methods for Resilient Aggregation and Safe Point Computation in Adversarial Multiagent Networks With Imprecise Data","authors":"Christopher A. Lee;Waseem Abbas","doi":"10.1109/TSIPN.2025.3594015","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3594015","url":null,"abstract":"This paper studies resilient data aggregation in multiagent networks subject to both adversarial agents and imprecise state observations. We show that existing algorithms, which assume exact state information, fail under such dual uncertainty. To address this, we propose a geometric approach that models each agent’s state as an imprecision region in <inline-formula><tex-math>$mathbb {R}^{d}$</tex-math></inline-formula> containing the true state. We present the <italic>Centerpoint of Imprecision Hulls (CPIH)</i> algorithm, which takes these regions—some corresponding to adversarial agents—as inputs and computes a point guaranteed to lie within the convex hull of the normal agents’ true states, despite unknown adversary identities and true state locations. We thoroughly analyze the algorithm’s theoretical guarantees and apply it to the resilient distributed vector consensus problem. Furthermore, we extend the framework to dynamic settings where these regions shrink as agents move closer together, deriving sufficient conditions for exact consensus in a multiagent network despite access to only imprecise states and adversarial presence. Numerical evaluations validate the method’s effectiveness.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1214-1227"},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed $H_infty$ Secure Fusion Estimation for Energy-Constrained Multi-Sensor Systems Under Hybrid Attacks 混合攻击下能量约束多传感器系统的分布式$H_infty$安全融合估计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-04 DOI: 10.1109/TSIPN.2025.3594164

Haiyu Song;Linyi Chen;Bo Chen;Wen-An Zhang;Li Yu

{"title":"Distributed $H_infty$ Secure Fusion Estimation for Energy-Constrained Multi-Sensor Systems Under Hybrid Attacks","authors":"Haiyu Song;Linyi Chen;Bo Chen;Wen-An Zhang;Li Yu","doi":"10.1109/TSIPN.2025.3594164","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3594164","url":null,"abstract":"This paper investigates the distributed <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> secure fusion estimation problem for energy-constrained multi-sensor systems subject to hybrid attacks. Given the limited energy supply, sensor nodes operate in two modes: high-energy mode, which ensures robust security during information transmission, and low-energy mode, which makes transmissions more vulnerable to hybrid attacks. The phenomenon of hybrid attacks is described as the stochastic occurrence of false data injection (FDI) and denial-of-service (DoS) attacks in the communication channels from sensors to local estimators. To handle these challenges, we propose a novel distributed <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> secure fusion estimation model designed specifically for energy-constrained multi-sensor systems under hybrid attacks scenarios. Subsequently, sufficient conditions are derived to ensure that the secure fusion estimation error system achieves exponential mean-square stability and <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> performance level. Additionally, the design of optimal fusion weight matrices is addressed. Finally, the effectiveness of the proposed distributed <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> secure fusion estimation method is demonstrated through an illustrative example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"994-1004"},"PeriodicalIF":3.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Graph Filters for Structure-Function Coupling Based Hub Node Identification 基于结构-功能耦合的集线器节点识别学习图滤波器

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-01 DOI: 10.1109/TSIPN.2025.3595070

Meiby Ortiz-Bouza;Duc Vu;Abdullah Karaaslanli;Selin Aviyente

{"title":"Learning Graph Filters for Structure-Function Coupling Based Hub Node Identification","authors":"Meiby Ortiz-Bouza;Duc Vu;Abdullah Karaaslanli;Selin Aviyente","doi":"10.1109/TSIPN.2025.3595070","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3595070","url":null,"abstract":"Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional brain networks. One such tool is hub node identification. Hubs are nodes within a network that link distinct brain units corresponding to specialized functional processes. Conventional methods for identifying hubs utilize different types of centrality measures and participation coefficient to profile various aspects of nodal importance. These methods solely rely on the functional connectivity networks constructed from functional magnetic resonance imaging (fMRI), ignoring the structure-function coupling in the brain. In this paper, we introduce a graph signal processing (GSP) based framework that utilizes both the structural connectivity and the functional activation to identify hubs. The proposed framework models functional activity as graph signals on the structural connectivity. Hub nodes are then detected based on the premise that they are sparse, have higher level of activity compared to their neighbors, and the non-hub nodes’ activity is the output of a low-pass graph filter. Based on these assumptions, an optimization framework, GraFHub, is formulated to learn the coefficients of the optimal graph filter and detect the hub nodes. The proposed framework is evaluated on both simulated data and resting state fMRI (rs-fMRI) data from Human Connectome Project (HCP).","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"980-993"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Smooth Graph Learning From Incomplete Data 基于不完整数据的在线平滑图学习

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-01 DOI: 10.1109/TSIPN.2025.3589719

Zehao Chen;Xiang Zhang;Muyun Zhou;Yinfei Xu;Chunguo Li;Baoyun Wang

{"title":"Online Smooth Graph Learning From Incomplete Data","authors":"Zehao Chen;Xiang Zhang;Muyun Zhou;Yinfei Xu;Chunguo Li;Baoyun Wang","doi":"10.1109/TSIPN.2025.3589719","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3589719","url":null,"abstract":"Graphs are essential for extracting crucial information embedded within structured data and are foundational tools across various fields. Predefined graphs, however, cannot adequately capture the intrinsic relationships within data, highlighting the need for learning graphs to construct meaningful representations. Particularly, graph learning is crucial in dynamic scenarios, where graphs evolve in response to streamed signals, requiring real-time adaptation through online methods. Additionally, missing values in sequential data pose challenges that necessitate signal reconstruction techniques to recover incomplete information, ensuring accurate and reliable graph inference. To address such issues, we design a novel online algorithm that achieves joint signal reconstruction and topology inference under smoothness priors. Specifically, the two sub-problems are formulated as a joint optimization task, solvable through alternating minimization. To enable efficient online graph learning with a trade-off in accuracy, the inexact proximal online gradient descent (IPOGD) is incorporated into our algorithm, and a dynamic regret analysis demonstrates a sublinear regret bound. Experimental results on both synthetic and real-world datasets validate its effectiveness in tracking slowly-evolving networks with incomplete data.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"872-887"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Temporal and Spatial Structures for Robust Rumor Detection in Social Networks 整合时空结构的社会网络稳健谣言检测

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-07-30 DOI: 10.1109/TSIPN.2025.3577317

Hui Li;Lai Wei;Kunquan Li;Guimin Huang;Jun Li

{"title":"Integrating Temporal and Spatial Structures for Robust Rumor Detection in Social Networks","authors":"Hui Li;Lai Wei;Kunquan Li;Guimin Huang;Jun Li","doi":"10.1109/TSIPN.2025.3577317","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3577317","url":null,"abstract":"In today’s highly informalized society, the speed and scope of rumor dissemination pose a great threat to social stability and personal interests. Detecting rumors manually requires a lot of human effort. Therefore, automatic rumor detection has received significant attention. Recently, some researchers have focused on using propagation structural features to identify rumors. However, existing propagation structure-based methods either utilize only spatial features or only temporal features of propagation. Few models can effectively leverage both types of propagation structural features. This paper proposes a Source-Guided Temporal-Spatial joint rumor detection model (SGTS). SGTS dynamically divides the propagation process of an event into a series of temporal sub-events. Additionally, SGTS employs an information-level connection strategy that incorporates spatial structural features from previous temporal stages into the encoding of subsequent stages. In this way, SGTS can effectively capture the spatiotemporal features of propagation. Experimental results and in-depth analysis on commonly-used datasets demonstrate that SGTS achieves significant improvements over existing methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"821-830"},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient Output Containment of Heterogeneous Multi-Agent Systems Against Byzantine Attacks 异构多智能体系统抗拜占庭攻击的弹性输出遏制

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-07-28 DOI: 10.1109/TSIPN.2025.3592314

Xin Gong;Yang Cao;Xiuxian Li;Hong Lin;Zhan Shu;Guanghui Wen

{"title":"Resilient Output Containment of Heterogeneous Multi-Agent Systems Against Byzantine Attacks","authors":"Xin Gong;Yang Cao;Xiuxian Li;Hong Lin;Zhan Shu;Guanghui Wen","doi":"10.1109/TSIPN.2025.3592314","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3592314","url":null,"abstract":"This study focuses on addressing distributed Byzantine-resilient output containment issues for heterogeneous continuous-time multi-agent systems. Inspired by the digital twin technology which creates a virtual replica of a physical object or system, a virtual layer named twin layer is introduced in this work, which is parallel to the conventional cyber-physical layer. The twin layer is more secure than the cyber-physical layer, which generates the secure reference trajectory of each agent via real-time data processing and simulation. Moreover, it decouples the resilient output containment against Byzantine attacks (BA) into two defense sub-schemes: One on the twin layer against Byzantine edge attacks (sending wrong and different messages to neighbors) and the other on the cyber-physical layer against Byzantine node attacks (falsifying input signals). On the twin layer, we develop a topology-assignable distributed resilient estimator by utilizing a novel secure centroid approach, which enhances the resilience of the twin layer by adding a minimal fraction of trusted edges. It is proved that achieving strong <inline-formula><tex-math>$[({n+1})f+1]$</tex-math></inline-formula>-robustness towards the leader set is adequate for ensuring the resilience of the twin layer. On the cyber-physical layer, we design a decentralized adaptive controller against Byzantine node attacks and can also handle potential inter-layered controller faults. This novel adaptive controller has the merit of converging exponentially at an adjustable rate, whose error bound can be explicitly stated. Consequently, we manage to address the resilient containment problem against BAs, in which the agents subject to Byzantine node attacks can also achieve output containment instead of just the normal agents. The simulation examples confirm the efficacy of this newly developed hierarchical protocol, where both normal and Byzantine followers converge within the dynamic convex hull formed by the normal leaders.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"938-951"},"PeriodicalIF":3.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and Robust Continual Graph Learning for Graph Classification in Biology 高效鲁棒连续图学习在生物图分类中的应用

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-07-24 DOI: 10.1109/TSIPN.2025.3592321

Ding Zhang;Jane Downer;Can Chen;Ren Wang

引用次数: 0

Probability-Constrained Distributed Non-Fragile Estimation Over Sensor Networks Subject to Stochastic Communication Protocol 随机通信协议下传感器网络的概率约束分布式非脆性估计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-07-24 DOI: 10.1109/TSIPN.2025.3592332

Jian Liu;Wenqiang Wang;Bing Hu;Jinliang Liu;Engang Tian;Jie Cao

{"title":"Probability-Constrained Distributed Non-Fragile Estimation Over Sensor Networks Subject to Stochastic Communication Protocol","authors":"Jian Liu;Wenqiang Wang;Bing Hu;Jinliang Liu;Engang Tian;Jie Cao","doi":"10.1109/TSIPN.2025.3592332","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3592332","url":null,"abstract":"This article focuses on the probability-constrained distributed non-fragile (PDNF) estimation problem for nonlinear time-varying systems with unknown but bounded noises, sensor saturation and uniform quantization over sensor networks (SNs). Owing to the limited bandwidth resources, stochastic communication protocol (SCP) is employed to manage network transmission and prevent data collision. At each transmission instant, the sensor node is allowed to communicate with only one randomly selected neighboring sensor. Meanwhile, the non-fragility of the estimator is taken into account to handle potential parameter variations. The goal of this article is to develop a PDNF estimation algorithm such that 1) the estimation error is confined within a certain ellipsoidal region with a predefined probability; and 2) the resulting error ellipsoid is minimized in the sense of matrix trace to achieve optimal estimation performance. In light of this, the sufficient criteria for the availability of the estimator are derived through recursive linear matrix inequality (RLMI) technique. Furthermore, the optimal estimator parameters are attained by solving a convex optimization problem. Ultimately, two simulation experiments are presented to validate the feasibility and practicality of the designed estimation algorithm.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"888-900"},"PeriodicalIF":3.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0