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

Distributed Online Learning Over Multitask Networks With Rank-One Model 基于rank - 1模型的多任务网络分布式在线学习

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 时间顶点和边缘信号的自适应联合估计

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 时变异构网络的动态事件触发弹性协调

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 希尔伯特空间中的分数阶傅里叶变换

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

Community Detection From Multiple Observations: From Product Graph Model to Brain Applications 从多个观察中发现社区：从产品图模型到大脑应用

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 存在异常值的图学习的惩罚似然方法

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 半分散网络上的高效通信联邦优化

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 DeepTrace：用图神经网络学习优化传染病网络中的接触者追踪

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 二阶参与者的全分布博弈策略及其在网络电力市场中的应用

IF 3 3区计算机科学

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

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

引用次数: 0