IEEE Transactions on Signal and Information Processing over Networks最新文献

Distributed Least Mean Square Estimation With Communication Noises Over Random Graphs 随机图上有通信噪声的分布式最小均方估计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-19 DOI: 10.1109/TSIPN.2025.3536103

Xiaozheng Fu;Siyu Xie;Tao Li

{"title":"Distributed Least Mean Square Estimation With Communication Noises Over Random Graphs","authors":"Xiaozheng Fu;Siyu Xie;Tao Li","doi":"10.1109/TSIPN.2025.3536103","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3536103","url":null,"abstract":"For the online distributed estimation problem of time-varying parameters, we study a linear regression model with measurement noises over time-varying random graphs. We propose a distributed normalized least mean square (LMS) algorithm, where each node updates its own estimate by the least mean square term, and sums the differences between its own estimate and the estimates of its neighbors with additive and multiplicative communication noises by the consensus term. By the algebraic graph theory and the stochastic analysis techniques, we obtain sufficient conditions for the boundedness of the tracking error. For a sequence of general random graphs, if the random graphs and the regression matrices satisfy the stochastic spatio-temporal persistence of excitation condition, then the mean-square tracking error is bounded by choosing appropriate constant gains. Furthermore, for conditional balanced graphs and Markovian switching graphs, we give sufficient conditions such that the persistence of excitation condition holds. Finally, we illustrate the effectiveness of the theoretical results through a numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"289-303"},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655014","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

Multi-Sensor State Estimation With a Sequential Stochastic Event-Triggered Mechanism 利用顺序随机事件触发机制进行多传感器状态估计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-10 DOI: 10.1109/TSIPN.2025.3546477

Zhongyao Hu;Bo Chen;Rusheng Wang;Li Yu

引用次数: 0

Fuzzy Aggregation and Label Propagation Based Social Community Detection Using Cuckoo Search

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-07 DOI: 10.1109/TSIPN.2025.3548427

Vishal Srivastava

{"title":"Fuzzy Aggregation and Label Propagation Based Social Community Detection Using Cuckoo Search","authors":"Vishal Srivastava","doi":"10.1109/TSIPN.2025.3548427","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3548427","url":null,"abstract":"A node in a social network is not part of just a cohesive group; it may be classified in many close or different communities. The community detection problem in social networks is similar to the clustering in networks where information is stored in node attributes and network structures. The Node-based features are often used in unsupervised algorithms that partially detect the local and overlapping communities. Networks displaying a community structure may exhibit hierarchical communities as well. Identification of hierarchical communities with community structure is a challenging task. This paper presents a two-step framework, i.e., aggregation and label propagation, to identify crisp and non-overlapping communities. Aggregation is an expansion-dissolution technique that results in crisp communities that don't require any prior information. We initially estimated random communities for each node and applied aggregation to improve them. The second step is the label propagation-based objective maximization method that takes improved crisp communities as fuzzy matrices and results in non-overlapping communities. The label propagation method is a label update mechanism for nodes with neighbors in a different community. The Label propagation function is maximized using cuckoo search and reported optimized communities. The two-step framework is empirically tested on real and simulated social networks. A comparative and contrast study is performed using performance and accuracy-based metrics to validate the framework and is found to be state-of-the-art.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"304-313"},"PeriodicalIF":3.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676124","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 Adaptive Signal Fusion for Fractional Programs

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-27 DOI: 10.1109/TSIPN.2025.3546462

Cem Ates Musluoglu;Alexander Bertrand

{"title":"Distributed Adaptive Signal Fusion for Fractional Programs","authors":"Cem Ates Musluoglu;Alexander Bertrand","doi":"10.1109/TSIPN.2025.3546462","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3546462","url":null,"abstract":"The distributed adaptive signal fusion (DASF) is an algorithmic framework that allows solving spatial filtering optimization problems in a distributed and adaptive fashion over a bandwidth-constrained wireless sensor network. The DASF algorithm requires each node to sequentially build a compressed version of the original network-wide problem and solve it locally. However, these local problems can still result in a high computational load at the nodes, especially when the required solver is iterative. In this paper, we study the particular case of fractional programs, i.e., problems for which the objective function is a fraction of two continuous functions, which indeed require such iterative solvers. By exploiting the structure of a commonly used method for solving fractional programs and interleaving it with the iterations of the standard DASF algorithm, we obtain a distributed algorithm with a significantly reduced computational cost compared to the straightforward application of DASF as a meta-algorithm. We prove convergence and optimality of this “fractional DASF” (F-DASF) algorithm and demonstrate its performance via numerical simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"258-273"},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637844","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 Cascaded Spline-Based Adaptive Graph Filters for Nonlinear Systems: Design and Performance Analysis

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-27 DOI: 10.1109/TSIPN.2025.3546469

Peng Cai;Dongyuan Lin;Junhui Qian;Yunfei Zheng;Zhongyuan Guo;Shiyuan Wang

{"title":"Distributed Cascaded Spline-Based Adaptive Graph Filters for Nonlinear Systems: Design and Performance Analysis","authors":"Peng Cai;Dongyuan Lin;Junhui Qian;Yunfei Zheng;Zhongyuan Guo;Shiyuan Wang","doi":"10.1109/TSIPN.2025.3546469","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3546469","url":null,"abstract":"The distributed nonlinear adaptive graph filter (DNAGF) is developed with the single nonlinear graph filter model (NGFM) to handle streaming datasets. However, the current DNAGFs tend to underperform when predicting unknown nonlinear dynamic systems. This suboptimal performance is due to their reliance on a single NGFM and the network's limited computational burden. To address these issues, two novel cascaded DNAGFs considering the spline interpolation method, i.e. a distributed Wiener spline adaptive graph filter (DWSAGF) and distributed Hammerstein spline adaptive graph filter (DHSAGF), are proposed to improve the capacity for nonlineaprediction in this paper. By utilizing piecewise low-order nonlinear spline functions, the proposed DWSAGF and DHSAGF can adapt locally to improve the fitting of the predicted nonlinear system to the unknown one. In DWSAGF and DHSAGF, the cascaded architectures containing linear and nonlinear subsystems are employed, which are more flexible than the single NGFM. Particularly, since DHSAGF has a memory through the constructed matrix <inline-formula><tex-math>${boldsymbol {bar{U}}}_{m}^{bar{t}}(r)$</tex-math></inline-formula>, it generates higher performance than DWSAGF for complex or time-varying nonlinear systems. In addition, the detailed performance analyses regarding DWSAGF and DHSAGF in the mean and mean-square senses are presented. Simulations are exhibited to validate the theoretical analysis and to show the performance superiorities of DWSAGF and DHSAGF.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"274-288"},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637851","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 Online Learning Over Multitask Networks With Rank-One Model

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-26 DOI: 10.1109/TSIPN.2025.3543973

Yitong Chen;Danqi Jin;Jie Chen;Cédric Richard;Wen Zhang;Gongping Huang;Jingdong Chen

引用次数: 0

Adaptive Joint Estimation of Temporal Vertex and Edge Signals

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-14 DOI: 10.1109/TSIPN.2025.3536084

Yi Yan;Tian Xie;Ercan E. Kuruoglu

{"title":"Adaptive Joint Estimation of Temporal Vertex and Edge Signals","authors":"Yi Yan;Tian Xie;Ercan E. Kuruoglu","doi":"10.1109/TSIPN.2025.3536084","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3536084","url":null,"abstract":"The adaptive estimation of coexisting temporal vertex (node) and edge signals on graphs is a critical task when a change in edge signals influences the temporal dynamics of the vertex signals. Existing Graph Signal Processing algorithms have extensively studied signals on graph vertices, and while recent advancements have started exploring signals on edges, a framework for systematically representing interactive time-varying signals across vertices, edges, and higher-order structures has yet to be fully realized. We propose an Adaptive Joint Vertex-Edge Estimation (AJVEE) algorithm for jointly estimating time-varying vertex and edge signals through a time-varying regression, incorporating both vertex signal filtering and edge signal filtering. Accompanying AJVEE is a newly proposed Adaptive Least Mean Square procedure based on the Hodge Laplacian (ALMS-Hodge), which is inspired by classical adaptive filters combining simplicial filtering and simplicial regression. AJVEE is able to operate jointly on the vertices and edges by merging two ALMS-Hodge specified on the vertices and edges into a unified formulation. A more generalized case extending AJVEE beyond the vertices and edges is being discussed. Experimenting on real-world traffic networks and population mobility networks, we have confirmed that our proposed AJVEE algorithm could accurately and jointly track time-varying vertex and edge signals on graphs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"215-229"},"PeriodicalIF":3.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553483","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

Dynamic Event-Triggering Resilient Coordination for Time-Varying Heterogeneous Networks

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-13 DOI: 10.1109/TSIPN.2025.3541932

Zirui Liao;Jian Shi;Shaoping Wang;Yuwei Zhang;Rentong Chen;Zhiyong Sun

引用次数: 0

The Graph Fractional Fourier Transform in Hilbert Space

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-13 DOI: 10.1109/TSIPN.2025.3540714

Yu Zhang;Bing-Zhao Li

引用次数: 0

Learning General Brain Network Representations of Different Brain Disorders Using Invariant Subgraph GNN 利用不变子图 GNN 学习不同脑部疾病的一般脑网络表征

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-11 DOI: 10.1109/TSIPN.2025.3540709

Hao Zhang;Ran Song;Liping Wang;Lei Mou;Yushan Lu;Yitian Zhao;Wei Zhang

{"title":"Learning General Brain Network Representations of Different Brain Disorders Using Invariant Subgraph GNN","authors":"Hao Zhang;Ran Song;Liping Wang;Lei Mou;Yushan Lu;Yitian Zhao;Wei Zhang","doi":"10.1109/TSIPN.2025.3540709","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3540709","url":null,"abstract":"Distribution shifts across data from various brain disorders pose significant challenges for diagnosis. Establishing general feature representations that can handle these distribution shifts is crucial for accurately diagnosing these conditions. However, this area remains largely unexplored. This work propose an Invariant Subgraph GNN (IS-GNN) to learn general brain network representations for classifying various brain disorders in resting-state fMRI. This model employs an invariant subgraph learning mechanism to capture invariant brain graphs and handle distribution shifts. Moreover, we have developed an adaptive structure perception module to improve the detection of invariant subgraph features in brain networks by assessing the importance of nodes within the brain graph. To further refine the model, we propose a self-supervised loss for invariant subgraph learning, ensuring the generation of invariant brain network representations. Pretrained on data from 1,943 subjects across three public datasets corresponding to Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Parkinson's Disease, the fine-tuning experiments of our proposed method demonstrate that the model achieves the state-of-the-art classification performance on not only the three datasets but also on an external Alzheimer's Disease dataset across.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"230-241"},"PeriodicalIF":3.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594439","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