IEEE Transactions on Signal and Information Processing over Networks最新文献_第7页

Distributed Optimization of Heterogeneous Linear Multi-Agent Systems With Unknown Disturbances and Optimal Gain Tuning 具有未知扰动的异构线性多智能体系统的分布优化与最优增益调谐

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-29 DOI: 10.1109/TSIPN.2025.3574852

Mengmeng Duan;Shanying Zhu;Ziwen Yang;Cailian Chen;Xinping Guan

{"title":"Distributed Optimization of Heterogeneous Linear Multi-Agent Systems With Unknown Disturbances and Optimal Gain Tuning","authors":"Mengmeng Duan;Shanying Zhu;Ziwen Yang;Cailian Chen;Xinping Guan","doi":"10.1109/TSIPN.2025.3574852","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3574852","url":null,"abstract":"In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. To solve this problem, we propose a distributed controller design framework, which reduces the controller design for heterogeneous linear multi-agent systems to the stabilizer design for first-order multi-agent systems. By this framework, we propose two kinds of dynamic controllers under the strong convexity of the objective function and the restricted secant inequality condition, respectively. Based on the optimal condition and singular perturbation analysis technique, we prove that the system converges to the optimal state if the disturbances tend to be constant or vary slowly. To further optimize the performance criterion under system stability and input constraint, we provide an optimal gain tuning algorithm such that the system stability, optimality and feasibility are simultaneously achieved. Numerical examples are provided to illustrate the effectiveness of the theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"563-576"},"PeriodicalIF":3.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272947","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

A Novel Asymmetric Functional Approach on Sampled-Data-Based Exponential Consensus of Nonlinear Multi-Agent Systems Against FDI Attacks 基于采样数据的非线性多智能体系统抗FDI攻击指数一致性的一种新的非对称泛函方法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-29 DOI: 10.1109/TSIPN.2025.3574872

V. M. Janani;K. Subramanian;P. Muthukumar;Hieu Trinh

{"title":"A Novel Asymmetric Functional Approach on Sampled-Data-Based Exponential Consensus of Nonlinear Multi-Agent Systems Against FDI Attacks","authors":"V. M. Janani;K. Subramanian;P. Muthukumar;Hieu Trinh","doi":"10.1109/TSIPN.2025.3574872","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3574872","url":null,"abstract":"This article investigates the exponential consensus in leaderless multi-agent systems (MASs) subject to Lipschitz nonlinearity, external perturbations, and missing measurements. First, an aperiodic nonfragile sampled-data control strategy is applied to the MASs in the presence of communication delays and randomly occurring false data injection attacks. This protocol provides robustness against controller gain fluctuations and enhances consensus performance with <inline-formula><tex-math>$H_infty$</tex-math></inline-formula> attenuation level. Next, unlike the existing studies, a novel exponential-type asymmetric Lyapunov-Krasovskii functional and a two-sided looped functional are constructed together with the relaxation of positive definiteness for an individual matrix. Utilizing these functionals, exponential consensus conditions are obtained within the form of linear matrix inequalities. Finally, using the YALMIP toolbox in MATLAB, three numerical examples validate theoretical outcomes exhibiting reduced conservatism with improved percentage of performance by maximizing the sampling period with a minimum number of decision variables compared with existing literature.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"551-562"},"PeriodicalIF":3.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272919","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

CoRNI: A Co-Evolutionary Framework Integrating Reputation and Network Structure for Modeling Social Influence Dynamics 一个整合声誉和网络结构的社会影响动力学模型的协同进化框架

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-28 DOI: 10.1109/TSIPN.2025.3572295

Hangjing Zhang;H. Vicky Zhao;Yixin Dai

{"title":"CoRNI: A Co-Evolutionary Framework Integrating Reputation and Network Structure for Modeling Social Influence Dynamics","authors":"Hangjing Zhang;H. Vicky Zhao;Yixin Dai","doi":"10.1109/TSIPN.2025.3572295","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3572295","url":null,"abstract":"The prevalence of Internet platforms, such as social media and web pages, enables users to share their opinions and observe each other's actions. Users may show strong advocacy and support for each other's opinions and decisions at one moment, while they may disagree with each other with polarized views later. These dynamic changes of supportive relationships pose challenges to influential entities such as government agencies, firms, politicians, experts and weblebrities, who aim to gain support from and have large influence on the public. To study this dynamics, we consider the Social Networks with Supportive Relationships (SN-SR), whose links represent actively supportive relationships and are disconnected when users disagree with each other. For these influential entities, their reputation and social influence impact each other's evolution over SN-SR, while few works study how to model this co-evolving pattern and how to analyze and predict the dynamics of their influence over networks. In this work, we use the network topology of the SN-SR as an intermediate variable to model and study the interplay between the reputation and influence, and propose an integrated framework called CoRNI to theoretically analyze its impact on social influence. We use simulations on synthetic networks and real Weibo data to validate our proposed model and theoretical analysis. This investigation provides important guidelines for influential entities to adjust their actions and improve their influence over the network.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"474-489"},"PeriodicalIF":3.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219659","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

Graph Laplacian Learning With Exponential Family Noise 具有指数族噪声的图拉普拉斯学习

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-26 DOI: 10.1109/TSIPN.2025.3572698

Changhao Shi;Gal Mishne

{"title":"Graph Laplacian Learning With Exponential Family Noise","authors":"Changhao Shi;Gal Mishne","doi":"10.1109/TSIPN.2025.3572698","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3572698","url":null,"abstract":"Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, they are restricted to learning from either smooth graph signals or with simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet remain under-explored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to include an offset variable which models different levels of variation of the nodes. Since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"641-654"},"PeriodicalIF":3.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581487","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

scGraPhT: Merging Transformers and Graph Neural Networks for Single-Cell Annotation 用于单细胞注释的合并变压器和图神经网络

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-26 DOI: 10.1109/TSIPN.2025.3573591

Emirhan Koç;Emre Kulkul;Gülara Kaynar;Tolga Çukur;Murat Acar;Aykut Koç

{"title":"scGraPhT: Merging Transformers and Graph Neural Networks for Single-Cell Annotation","authors":"Emirhan Koç;Emre Kulkul;Gülara Kaynar;Tolga Çukur;Murat Acar;Aykut Koç","doi":"10.1109/TSIPN.2025.3573591","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3573591","url":null,"abstract":"The invention of single-cell RNA sequencing (scRNA-seq) has enabled transcriptomic examination of cells on an individual basis, uncovering cell-to-cell phenotypic heterogeneity within isogenic cell populations. Inevitably, cell type annotation has emerged as a fundamental, albeit challenging task in scRNA-seq data analysis, which involves identifying and characterizing cells based on their unique molecular profiles. Recently, deep learning techniques with their data-driven priors have shown significant promise in this task. On the one hand, task-agnostic transformers pre-trained on large-scale biological databases capture generalizable representations but cannot characterize intricate relationships between genes and cells. Contrarily, task-specific graph neural networks (GNNs) trained on target datasets can characterize entity relationships, but they can suffer from poor generalizability. Furthermore, existing GNNs focus on either homogeneous or heterogeneous relationships, failing to capture the full cellular complexity. Here, we propose scGraPhT, a unified transformer–graph model that combines pre-trained transformer embeddings of scRNA-seq data with a multilayer GNN to capture cell-cell, cell-gene, and gene-gene relationships. Different from previous GNNs, scGraPhT examines both homogeneous and heterogeneous relationships through subgraph layers to offer a more comprehensive assessment. Since the graph construction uses transformer-derived embeddings, scGraPhT does not require costly training procedures and can also be adapted to leverage any transformer-based single-cell annotation method, such as scGPT or scBERT. Demonstrations on three scRNA-seq benchmark datasets indicate that scGraPhT outperforms state-of-the-art annotation methods without compromising efficiency. Utilizing Grad-CAM, we demonstrate how the GNN and transformer components complement each other to enhance performance. We share our source codes and datasets for reproducibility.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"505-519"},"PeriodicalIF":3.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255696","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

Joint Design of Sample and Node Weighting for Distributed Ordinal Regression Under Label Noise 标签噪声下分布有序回归的样本和节点加权联合设计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-21 DOI: 10.1109/TSIPN.2025.3572292

Huan Liu;Xiaoxian Lao;Jiankai Tu;Chunguang Li

{"title":"Joint Design of Sample and Node Weighting for Distributed Ordinal Regression Under Label Noise","authors":"Huan Liu;Xiaoxian Lao;Jiankai Tu;Chunguang Li","doi":"10.1109/TSIPN.2025.3572292","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3572292","url":null,"abstract":"Ordinal regression (OR) is a category of special classification problem where the labels have natural orders. In some practical OR applications, data may be independently collected by multiple agencies. Due to privacy protection or some other constraints, centralized processing is not feasible, and distributed methods are more suitable. Besides, the collected data may have noisy labels, and the noise levels at each agency could be different because of the difference in data collection environments among agencies. A series of works adopt weighting strategies based on label quality to handle label noise in distributed scenarios. Some of them consider intra-node sample-level weighting while ignoring the difference in label noise levels among nodes. Some others consider node-level weighting while not eliminating the impact of label noise inside a node. In fact, sample weighting and node weighting are interrelated, and can interact with each other to improve overall performance. In this paper, we propose a joint design of sample and node weighting (JSNW) for distributed OR under different levels of label noise across nodes. In JSNW, sample and node weighting interact with each other to provide adaptive sample and node weights for model update to mitigate the impact of label noise. Theoretically, we prove the rank consistency of JSNW. Experimentally, the results demonstrate the effectiveness of the joint design and show that JSNW outperforms several state-of-the-art methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"490-504"},"PeriodicalIF":3.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219674","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

Inhomogeneous Graph Trend Filtering via a $ell _{2,0}$-Norm Cardinality Penalty 基于$ well _{2,0}$-Norm基数惩罚的非齐次图趋势过滤

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-21 DOI: 10.1109/TSIPN.2025.3553025

Xiaoqing Huang;Andersen Ang;Kun Huang;Jie Zhang;Yijie Wang

引用次数: 0

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 基于模糊聚合和标签传播的杜鹃搜索社会社区检测

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