IEEE Transactions on Signal and Information Processing over Networks最新文献_第2页

Community Detection From Multiple Observations: From Product Graph Model to Brain Applications

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.2025.3540702

Tiziana Cattai;Gaetano Scarano;Marie-Constance Corsi;Fabrizio DeVico Fallani;Stefania Colonnese

{"title":"Community Detection From Multiple Observations: From Product Graph Model to Brain Applications","authors":"Tiziana Cattai;Gaetano Scarano;Marie-Constance Corsi;Fabrizio DeVico Fallani;Stefania Colonnese","doi":"10.1109/TSIPN.2025.3540702","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3540702","url":null,"abstract":"This paper proposes a multilayer graph model for community detection based on multiple observations. This scenario is common when different estimators are used to infer graph edges from signals at the nodes, or when various signal measurements are taken. The multilayer network stacks these graph observations at different layers and links replica nodes at adjacent layers. This configuration corresponds to the Cartesian product between the ground truth graph and a path graph, where the number of nodes matches the number of observations. Using the algebraic structure of the Laplacian of the Cartesian multilayer network, we infer a subset of the eigenvectors of the true graph and perform community detection. Experimental results on synthetic graphs demonstrate the accuracy of the method, which outperforms state-of-the-art approaches in correctly detecting graph communities. Finally, we apply our method to distinguish between different brain networks derived from real EEG data collected during motor imagery experiments. We conclude that our approach is promising for identifying graph communities when multiple graph observations are available, and it shows potential for applications such as EEG-based motor imagery applications.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"201-214"},"PeriodicalIF":3.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553482","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

Penalized Likelihood Approach for Graph Learning in the Presence of Outliers

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-10 DOI: 10.1109/TSIPN.2025.3540701

Ghania Fatima;Petre Stoica;Prabhu Babu

引用次数: 0

Communication-Efficient Federated Optimization Over Semi-Decentralized Networks

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-06 DOI: 10.1109/TSIPN.2025.3539004

He Wang;Yuejie Chi

{"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}

引用次数: 0

DeepTrace: Learning to Optimize Contact Tracing in Epidemic Networks With Graph Neural Networks

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-06 DOI: 10.1109/TSIPN.2025.3530346

Chee Wei Tan;Pei-Duo Yu;Siya Chen;H. Vincent Poor

{"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}

引用次数: 0

Fully Distributed Game Strategy for Second-Order Players and Its Application to Networked Electricity Markets

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-05 DOI: 10.1109/TSIPN.2025.3538996

Zhenhua Deng;Minghuan Ye;Xiang-Peng Xie;Xiaojun Yang

引用次数: 0

A Novel Spectral Graph Distance Measure and Its Applications in Biomedical Signal Processing

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-29 DOI: 10.1109/TSIPN.2025.3536085

Jyoti Maheshwari;Shiv Dutt Joshi;Tapan K Gandhi

引用次数: 0

Variational Graph Neural Network Based on Normalizing Flows

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-17 DOI: 10.1109/TSIPN.2025.3530350

Hailong Su;Zhipeng Li;Chang-An Yuan;Vladimir F. Filaretov;Deshuang Huang

{"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}

引用次数: 0

Multiview Graph Learning With Consensus Graph

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-08 DOI: 10.1109/TSIPN.2025.3527888

Abdullah Karaaslanli;Selin Aviyente

{"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}

引用次数: 0

Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data 动态物理传感器网络数据的鲁棒时变图信号恢复

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-06 DOI: 10.1109/TSIPN.2025.3525978

Eisuke Yamagata;Kazuki Naganuma;Shunsuke Ono

{"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}

引用次数: 0

Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning 半监督学习的标签引导图优化卷积网络

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-06 DOI: 10.1109/TSIPN.2025.3525961

Ziyan Zhang;Bo Jiang;Jin Tang;Bin Luo

{"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}

引用次数: 0