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

{"title":"Communication-Efficient Federated Optimization Over Semi-Decentralized Networks","authors":"He Wang;Yuejie Chi","doi":"10.1109/TSIPN.2025.3539004","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3539004","url":null,"abstract":"In large-scale federated and decentralized learning, communication efficiency is one of the most challenging bottlenecks. While gossip communication—where agents can exchange information with their connected neighbors—is more cost-effective than communicating with the remote server, it often requires a greater number of communication rounds, especially for large and sparse networks. To tackle the trade-off, we examine the communication efficiency under a <italic>semi-decentralized</i> communication protocol, in which agents can perform both agent-to-agent and agent-to-server communication <italic>in a probabilistic manner</i>. We design a tailored communication-efficient algorithm over semi-decentralized networks, referred to as <monospace>PISCO</monospace>, which inherits the robustness to data heterogeneity thanks to gradient tracking and allows multiple local updates for saving communication. We establish the convergence rate of <monospace>PISCO</monospace> for nonconvex problems and show that <monospace>PISCO</monospace> enjoys a linear speedup in terms of the number of agents and local updates. Our numerical results highlight the superior communication efficiency of <monospace>PISCO</monospace> and its resilience to data heterogeneity and various network topologies.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"147-160"},"PeriodicalIF":3.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489163","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}

{"title":"DeepTrace: Learning to Optimize Contact Tracing in Epidemic Networks With Graph Neural Networks","authors":"Chee Wei Tan;Pei-Duo Yu;Siya Chen;H. Vincent Poor","doi":"10.1109/TSIPN.2025.3530346","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3530346","url":null,"abstract":"Digital contact tracing aims to curb epidemics by identifying and mitigating public health emergencies through technology. Backward contact tracing, which tracks the sources of infection, proved crucial in places like Japan for identifying COVID-19 infections from superspreading events. This paper presents a novel perspective on digital contact tracing by modeling it as an online graph exploration problem, framing forward and backward tracing strategies as maximum-likelihood estimation tasks that leverage iterative sampling of epidemic network data. The challenge lies in the combinatorial complexity and rapid spread of infections. We introduce <italic>DeepTrace</i>, an algorithm based on a Graph Neural Network that iteratively updates its estimations as new contact tracing data is collected, learning to optimize the maximum likelihood estimation by utilizing topological features to accelerate learning and improve convergence. The contact tracing process combines either BFS or DFS to expand the network and trace the infection source, ensuring efficient real-time exploration. Additionally, the GNN model is fine-tuned through a two-phase approach: pre-training with synthetic networks to approximate likelihood probabilities and fine-tuning with high-quality data to refine the model. Using COVID-19 variant data, we illustrate that <italic>DeepTrace</i> surpasses current methods in identifying superspreaders, providing a robust basis for a scalable digital contact tracing strategy.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"97-113"},"PeriodicalIF":3.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360949","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}

{"title":"Fully Distributed Game Strategy for Second-Order Players and Its Application to Networked Electricity Markets","authors":"Zhenhua Deng;Minghuan Ye;Xiang-Peng Xie;Xiaojun Yang","doi":"10.1109/TSIPN.2025.3538996","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3538996","url":null,"abstract":"In this paper, we study the noncooperative games (NGs) of multi-agent systems. In our problem, the players have private payoff functions, and their decisions are subject to local and coupling nonlinear inequality constraints. Moreover, our problem contains second-order dynamical systems of players. To control these second-order players to autonomously participate in the games, a distributed adaptive strategy is proposed based on state feedback and primal-dual methods. With our method, the updates of the control inputs of all players depend only on their own and neighbors' information, and are independent of global parameters or variables, different from other related methods. By virtue of variational analysis and LaSalle invariance principle, it is proved that our strategy converges to the variational Generalized Nash Equilibrium (v-GNE) of the games. Finally, the proposed method is applied to networked electricity market games of smart grids.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"136-146"},"PeriodicalIF":3.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465807","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}

{"title":"Two-View and Multi-View Tensor Canonical Correlation Analysis Over Graphs","authors":"Thummaluru Siddartha Reddy;Sundeep Prabhakar Chepuri","doi":"10.1109/TSIPN.2025.3536102","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3536102","url":null,"abstract":"In this work, we focus on the multi-view dimensionality reduction problem for tensor data on graphs. In particular, we extend canonical correlation analysis on graphs <monospace>(CCA-G)</monospace> and multi-view canonical correlation analysis on graphs <monospace>(MCCA-G)</monospace> for tensor data. Directly applying <monospace>CCA-G</monospace> and <monospace>MCCA-G</monospace> to tensor data requires vectorization, which destroys the underlying structure in the data and often outputs very high-dimensional data leading to the curse of dimensionality. To circumvent the vectorization operation, we propose tensor canonical correlation analysis on graphs <monospace>(TCCA-G)</monospace> for two view data and tensor multi-view canonical correlation analysis on graphs <monospace>(TMCCA-G)</monospace> for multi-view tensor data that preserves the intrinsic structure in data and accounts for underlying graph structure in the latent variable. In particular, the proposed <monospace>TCCA-G</monospace> promotes smoothness of the tensor canonical variates over a graph and outputs the tensor canonical variates that are correlated within the set and uncorrelated across the sets. In the absence of prior (smoothness) information on the latent variable, <monospace>TCCA-G</monospace> simplifies to tensor canonical correlation analysis <monospace>(TCCA)</monospace> that only preserves the intrinsic structure in the data and results in an uncorrelated set of features. To solve <monospace>TCCA-G</monospace> and <monospace>TCCA</monospace>, we present an algorithm based on alternating minimization. In particular, the canonical subspaces in <monospace>TCCA</monospace> and <monospace>TCCA-G</monospace> are obtained by solving an eigenvalue problem. <monospace>TMCCA-G</monospace> extends <monospace>TCCA-G</monospace> to multi-view data, wherein <monospace>TMCCA-G</monospace> obtains the canonical subspaces by solving a simple least-squares problem and the common source is obtained recursively using a Crank-Nicolson-like update to preserve the orthonormality constraints. In the absence of the graph prior, we present tensor multi-view canonical correlation analysis <monospace>(TMCCA)</monospace>, in which the common source is obtained in closed-form by solving an orthogonal Procustes problem. Therefore, each subproblem in <monospace>TMCCA</monospace> admits a closed-form solution in contrast to <monospace>TMCCA-G</monospace>. We show the efficacy of proposed algorithms through experiments on diverse tasks such as classification and clustering on real datasets.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"535-550"},"PeriodicalIF":3.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272920","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}

{"title":"A Novel Spectral Graph Distance Measure and Its Applications in Biomedical Signal Processing","authors":"Jyoti Maheshwari;Shiv Dutt Joshi;Tapan K Gandhi","doi":"10.1109/TSIPN.2025.3536085","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3536085","url":null,"abstract":"In many real time scenarios, it becomes necessary to compare complex networks. Graph distances measure the degree of similarity between any two networks. We conjecture that construction of the edge weight matrix of any network should consider the underlying physics of the problem. In addition to that, the selection of eigenvectors chosen for analysis also play an important role in comparing any two networks. In this paper, we explore the most naturally formed networks in various real time applications, such as tracking the transitions of brain states and real time seizure onset detection, and propose a graph distance measure based on the spectral characteristics of the edge weight matrix. We have conducted several experiments to show the efficacy of the proposed graph distance measure. It has an application in comparing any two brain networks while studying brain dynamics. Our results clearly show that the proposed graph distance outperforms the existing graph distances.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"114-123"},"PeriodicalIF":3.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446213","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}

{"title":"Variational Graph Neural Network Based on Normalizing Flows","authors":"Hailong Su;Zhipeng Li;Chang-An Yuan;Vladimir F. Filaretov;Deshuang Huang","doi":"10.1109/TSIPN.2025.3530350","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3530350","url":null,"abstract":"Graph Neural Networks (GNNs) have recently achieved significant success in processing non-Euclidean datasets, such as social and protein-protein interaction networks. However, these datasets often contain inherent uncertainties, such as missing edges between nodes that are closely related. Variational Graph Auto-Encoders (VGAE) and other Bayesian methods have been proposed to address the problem. Unfortunately, they can't handle graph data effectively. VGAE, for instance, the posterior is assumed to be Gaussian, which can not match the true posterior well. To overcome these limitations, a normalizing flows(NFs) based on variational GNN is proposed in this paper. Unlike VGAE, our approach no longer assumes that the posterior distribution is a standard Gaussian distribution, but instead utilizes NFs to learn more complex and flexible distributions. NFs transforms simple distributions into complex ones through a series of invertible transformations. The transformed distribution is more flexible and can match the true distribution better. Specifically, in order to obtain the reversible transformer, inspired by RealNVP, affine transformations on graphs are used to map a simple distribution to a complex one. The transformed distribution can infer more complex distributions like skewed. We conduct experiments in the link prediction task and our method performs excellently compared with other methods and even achieves state-of-the-art results on some datasets.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"177-186"},"PeriodicalIF":3.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489281","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}

{"title":"Multiview Graph Learning With Consensus Graph","authors":"Abdullah Karaaslanli;Selin Aviyente","doi":"10.1109/TSIPN.2025.3527888","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3527888","url":null,"abstract":"Graph topology inference is a significant task in many application domains. Existing approaches are mostly limited to learning a single graph assuming that the observed data is homogeneous. This is problematic because many modern datasets are heterogeneous and involve multiple related graphs, i.e., multiview graphs. Prior work in multiview graph learning ensures the similarity of learned view graphs through pairwise regularization, which has several limitations. First, most of the existing work focuses on the Gaussian Graphical Models (GGM) which learns precision matrices rather than the actual graph structures. Second, these methods do not infer the consensus structure across views, which may be useful in certain applications for summarizing the group level connectivity patterns. Finally, the number of pairwise constraints increases quadratically with the number of views. To address these issues, we propose a consensus graph-based multiview graph model, where each view is assumed to be a perturbed version of an underlying consensus graph. The proposed framework assumes that the observed graph data is smooth over the multiview graph and learns the graph Laplacians. A generalized optimization framework to jointly learn the views and the consensus graph is proposed, where different regularization functions can be incorporated into the formulation based on the structure of the underlying consensus graph and the perturbation model. Experiments with simulated data show that the proposed method has better performance than existing GGM-based methods and requires less run time than pairwise regularization-based methods. The proposed framework is also employed to infer the functional brain connectivity networks of multiple subjects from their electroencephalogram (EEG) recordings, revealing both the consensus structure and the individual variation across subjects.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"161-176"},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489148","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}

{"title":"Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data","authors":"Eisuke Yamagata;Kazuki Naganuma;Shunsuke Ono","doi":"10.1109/TSIPN.2025.3525978","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3525978","url":null,"abstract":"We propose a time-varying graph signal recovery method for estimating the true time-varying graph signal from corrupted observations by leveraging dynamic graphs. Most of the conventional methods for time-varying graph signal recovery have been proposed under the assumption that the underlying graph that houses the signals is static. However, in light of rapid advances in sensor technology, the assumption that sensor networks are time-varying like the signals is becoming a very practical problem setting. In this paper, we focus on such cases and formulate dynamic graph signal recovery as a constrained convex optimization problem that simultaneously estimates both time-varying graph signals and sparsely modeled outliers. In our formulation, we use two types of regularizations, time-varying graph Laplacian-based and temporal difference-based, and also separately modeled missing values with known positions and unknown outliers to achieve robust estimations from highly degraded data. In addition, an algorithm is developed to efficiently solve the optimization problem based on a primal-dual splitting method. Extensive experiments on simulated drone remote sensing data and real-world sea surface temperature data demonstrate the advantages of the proposed method over existing methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"59-70"},"PeriodicalIF":3.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992951","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}

{"title":"Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning","authors":"Ziyan Zhang;Bo Jiang;Jin Tang;Bin Luo","doi":"10.1109/TSIPN.2025.3525961","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3525961","url":null,"abstract":"Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of existing GCNs are composed of two parts, i.e., feature propagation (FP) on a neighborhood graph and feature transformation (FT) with a fully connected network. For semi-supervised learning, existing GCNs generally utilize the label information only to train the parameters of the FT part via optimizing the loss function. However, they lack exploiting the label information in neighborhood feature propagation. Besides, due to the fixed graph topology used in FP, existing GCNs are vulnerable w.r.t. structural noises/attacks. To address these issues, we propose a novel and robust Label Guided Graph Optimized Convolutional Network (LabelGOCN) model which aims to fully exploit the label information in feature propagation of GCN via pairwise constraints propagation. In LabelGOCN, the pairwise constraints can provide a kind of ‘weakly’ supervised information to refine graph topology structure and thus to guide graph convolution operations for robust semi-supervised learning tasks. In particular, LabelGOCN jointly refines the pairwise constraints and GCN via a unified regularization model which can boost their respective performance. The experiments on several benchmark datasets show the effectiveness and robustness of the proposed LabelGOCN on semi-supervised learning tasks.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"71-84"},"PeriodicalIF":3.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992949","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}

{"title":"Event-Triggered Data-Driven Distributed LFC Using Controller-Dynamic-Linearization Method","authors":"Xuhui Bu;Yan Zhang;Yiming Zeng;Zhongsheng Hou","doi":"10.1109/TSIPN.2025.3525950","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3525950","url":null,"abstract":"This paper is concerned with an event-triggered distributed load frequency control method for multi-area interconnected power systems. Firstly, because of high dimension, nonlinearity and uncertainty of the power system, the relevant model information cannot be fully obtained. To realize the design of LFC algorithm under the condition that the model information is unknown, the equivalent functional relationship between the control signal and the area-control-error signal is established by using a dynamic linearization technique. Secondly, a novel distributed load frequency control algorithm is proposed based on controller dynamic-linearization method and the controller parameters are tuned online by constructing a radial basis function neural network. In addition, to reduce the computation and communication burden on the system, an event-triggered mechanism is also designed, in which whether the data is transmitted at the current instant is completely determined by a triggering condition. Rigorous analysis shows that the proposed method can render the frequency deviation of the power system to converge to a bounded value. Finally, simulation results in a four-area power system verify the effectiveness of the proposed algorithm.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"85-96"},"PeriodicalIF":3.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992950","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}