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

{"title":"Integrating Temporal and Spatial Structures for Robust Rumor Detection in Social Networks","authors":"Hui Li;Lai Wei;Kunquan Li;Guimin Huang;Jun Li","doi":"10.1109/TSIPN.2025.3577317","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3577317","url":null,"abstract":"In today’s highly informalized society, the speed and scope of rumor dissemination pose a great threat to social stability and personal interests. Detecting rumors manually requires a lot of human effort. Therefore, automatic rumor detection has received significant attention. Recently, some researchers have focused on using propagation structural features to identify rumors. However, existing propagation structure-based methods either utilize only spatial features or only temporal features of propagation. Few models can effectively leverage both types of propagation structural features. This paper proposes a Source-Guided Temporal-Spatial joint rumor detection model (SGTS). SGTS dynamically divides the propagation process of an event into a series of temporal sub-events. Additionally, SGTS employs an information-level connection strategy that incorporates spatial structural features from previous temporal stages into the encoding of subsequent stages. In this way, SGTS can effectively capture the spatiotemporal features of propagation. Experimental results and in-depth analysis on commonly-used datasets demonstrate that SGTS achieves significant improvements over existing methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"821-830"},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751049","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 Diffusion Recursive Algorithm for Distributed Widely-Linear Exponential Functional Link Network","authors":"Guobing Qian;Jiayin Wang;Luping Shen;Ying-Ren Chien;Junhui Qian;Shiyuan Wang","doi":"10.1109/TSIPN.2025.3589685","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3589685","url":null,"abstract":"Distributed adaptive filtering has emerged as a critical methodology across diverse application domains, including wireless sensor networks, distributed signal processing, and intelligent control systems. However, existing diffusion-based adaptive filters suffer performance degradation in non-Gaussian noise and complex network topologies, leading to sub-optimal operation and instability risks. These limitations motivate the development of a robust framework that maintains distributed processing advantages while improving noise robustness. To address this, we propose a distributed widely-linear exponential functional link network (D-WLEFLN) combining wide-linear architecture with exponential expansions for enhanced nonlinear modeling. Furthermore, we develop a kernel risk Blake- Zisserman (KRBZ) based cost function to achieve enhanced outlier resilience. Building upon this foundation, a diffusion recursive kernel risk Blake-Zisserman (D-RKRBZ) algorithm is developed through recursive optimization, alongside a computationally efficient variant specifically designed for the WL architecture to maintain operational efficiency while preserving estimation accuracy. We provide theoretical analysis for the proposed algorithm, encompassing both mean stability and mean square performance. Simulation results validate that the performance of the proposed D-RKRBZ algorithm closely aligns with theoretical analysis. Comparative evaluations against existing diffusion counterparts reveal that D-RKRBZ can achieve lower mean square deviation (MSD) in complex-valued non-Gaussian environments, including contaminated Gaussian (CG) noise and <inline-formula><tex-math>$alpha $</tex-math></inline-formula> stable noise scenarios.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"845-858"},"PeriodicalIF":3.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750973","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":"Graph Distributional Signals for Regularization in Graph Neural Networks","authors":"Feng Ji;Yanan Zhao;See Hian Lee;Kai Zhao;Wee Peng Tay;Jielong Yang","doi":"10.1109/TSIPN.2025.3587400","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587400","url":null,"abstract":"In graph neural networks (GNNs), both node features and labels are examples of graph signals. While it is common in graph signal processing to impose signal smoothness constraints in learning and estimation tasks, it is unclear how this can be done for discrete node labels. We bridge this gap by introducing the concept of graph distributional signals. We work with the distributions of node labels instead of their values and propose notions of smoothness and non-uniformity of such graph distributional signals. We then propose a general regularization method for GNNs that allows us to encode distributional smoothness and non-uniformity of the model output in semi-supervised node classification tasks. Numerical experiments demonstrate that our method can significantly improve the performance of most base GNN models in different problem settings.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"670-682"},"PeriodicalIF":3.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657391","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":"Virtual Convex Hull Based Distributed Iterative Localization for Mobile Sensor Networks Under Denial-of-Service Attacks","authors":"Shaojie Yao;Lei Shi;Yong Wang;Rui Liu;Yi Zhou","doi":"10.1109/TSIPN.2025.3587404","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587404","url":null,"abstract":"This paper addresses the problem of cybersecurity in the localization process of mobile sensor networks. A generic model of random periodic denial-of-service (DoS) attacks is considered, where the attacker’s behavior is bounded only by the duration and frequency of the DoS attacks. A class of distributed localization algorithms based on virtual convex hull is proposed, which is abstracted as a linear time-varying system by constructing virtual convex hull using a single anchor node. Using the method combining graph composition and sub-stochastic matrix, it is shown that the algorithm can accurately converge to the true locations of sensor nodes. At last, the effectiveness of the algorithm is verified by simulation examples.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"780-793"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687679","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":"Process-Based Triggering and Accelerated Dual Averaging Algorithm for Dynamic Parameter Estimation","authors":"Yaoyao Zhou;Gang Chen;Zhenghua Chen","doi":"10.1109/TSIPN.2025.3587414","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587414","url":null,"abstract":"In the large-scale cyber-physical systems, to conserve communication resources holds critical significance, thereby driving extensive research interest toward distributed optimization algorithms with high communication efficiency. This paper investigates the constrained distributed dynamic parameter estimation problem (CDPE) for communication resource conservation, and further considers how to cope with more generally directed communication structure, unavoidable arbitrary bounded communication delays, and diverse update strategies. We introduce a new process-based triggering strategy and develop an efficient Process-based Triggering Accelerated Dual Averaging Algorithm(PTADA). Compared with the traditional time-dependent threshold, the PTADA can well adapt to the dynamic behavior of distributed optimization and save communication resources. Our dynamic bound is linear and is independent of the explicit time horizon. Moreover, we further extend PTADA to address scenarios where gradient information cannot be directly obtained, while ensuring no performance degradation. This extension can make the algorithm more realistic and universal. Finally, a distributed multi-sensor network is set up to verify the effectiveness of the algorithm.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"683-695"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680837","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":"E(Q)AGNN-PPIS: Attention Enhanced Equivariant Graph Neural Network for Protein-Protein Interaction Site Prediction","authors":"Animesh;Rishi Suvvada;Plaban Kumar Bhowmick;Pralay Mitra","doi":"10.1109/TSIPN.2025.3587396","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587396","url":null,"abstract":"Identifying protein binding sites, the specific regions on a protein’s surface where interactions with other molecules occur, is crucial for understanding disease mechanisms and facilitating drug discovery. Although numerous computational techniques have been developed to identify protein binding sites, serving as a valuable screening tool that reduces the time and cost associated with conventional experimental approaches, achieving significant improvements in prediction accuracy remains a formidable challenge. Recent advancements in protein structure prediction, notably through tools like AlphaFold, have made vast numbers of 3-D protein structures available, presenting an opportunity to enhance binding site prediction methods. The availability of detailed 3-D structures has led to the development of Equivariant Graph Neural Networks (GNNs), which can analyze complex spatial relationships in protein structures while maintaining invariance to rotations and translations. However, current equivariant GNN methods still face limitations in fully exploiting the geometric features of protein structures. To address this, we introduce E(Q)AGNN-PPIS, an Equivariant Attention-Enhanced Graph Neural Network designed for predicting protein binding sites by leveraging 3-D protein structure. Our method augments the Equivariant GNN framework by integrating an attention mechanism. This attention component allows the model to focus on the most relevant structural features for binding site prediction, significantly enhancing its ability to capture complex spatial patterns and interactions within the protein structure. Our experimental findings underscore the enhanced performance of E(Q)AGNN-PPIS compared to current state-of-the-art approaches, exhibiting gains of 8.33% in the Area Under the Precision-Recall Curve (AUPRC) and 10% in the Matthews Correlation Coefficient (MCC) across benchmark datasets. Additionally, our method demonstrates fast inference and robust generalization across proteins with varying sequence lengths, outperforming baseline methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"740-751"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687640","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":"RNGCNs: Robust Norm Graph Convolutional Networks in the Presence of Missing Data and Large Noises","authors":"Ziyan Zhang;Bo Jiang;Zhengzheng Tu;Bin Luo","doi":"10.1109/TSIPN.2025.3588087","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3588087","url":null,"abstract":"Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such as gross corruption and missing values.Existing graph convolutions (GCs) generally focus on feature propagation on structured-graph which i) fail to address the graph data with missing values and ii) often perform susceptibility to large feature errors/noises. To address this issue, in this paper, we propose to incorporate robust norm feature learning mechanism into graph convolution and present Robust Norm Graph Convolutions (RNGCs) for graph data in the presence of feature noises and missing values. Our RNGCs are proposed based on the interpretation of GCs from a propagation function aspect of ‘data reconstruction on graph’. Based on it, we then derive our RNGCs by exploiting robust norm based propagation functions into GCs. Finally, we incorporate the derived RNGCs into an end-to-end network architecture and propose kinds of RNGCNs for graph data learning. Experimental results on several noisy datasets demonstrate the effectiveness and robustness of the proposed RNGCNs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"859-871"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758446","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":"Secure Observer-Based $H_{infty }$ Synchronization for Singularly Perturbed Multiweighted Complex Networks With Stochastic Communication Protocol","authors":"Yan Li;Min Gao;Lijuan Zha;Jinliang Liu;Engang Tian;Chen Peng","doi":"10.1109/TSIPN.2025.3588094","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3588094","url":null,"abstract":"This paper addresses the problem of observer-based <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> synchronization control for singularly perturbed multiweighted complex networks (SPMCNs) with communication constraints and cyberattack threats. Firstly, given the limited communication bandwidth, a stochastic communication (SC) protocol is employed to deal with the potential data collision in each node of SPMCNs incurred by the mismatch between traffic load and resource availability. The SC protocol is specifically depicted by a Markov chain with partially known transition probabilities to improve its applicability. Then, the cybersecurity for SPMCNs is investigated, and the focus is concentrated on deception attacks due to they pose significant risks by maliciously tampering with sensitive information. Based on modeling the behavior of the considered deception attacks, observer-assisted synchronization controllers with undetermined gains are designed and an augmented synchronization error system is established. Subsequently, the stability with guaranteed <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control performance of the constructed system is analyzed, and then a feasible algorithm for determining the gains of the desired observers and controllers is provided. Finally, simulations are conducted based on an urban public traffic network to validate the efficiency and practicability of the proposed synchronization control scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"767-779"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687811","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":"Finite-Horizon Filtering for Networked 2-D Systems With Asynchronous Measurement Delays Under Non-Logarithmic Sensor Resolution","authors":"Yu Chen;Wei Wang;Zidong Wang;Chunyan Han;Shuxin Du","doi":"10.1109/TSIPN.2025.3587394","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587394","url":null,"abstract":"In this paper, the recursive state estimation problem is investigated for a class of two-dimensional networked shift-varying systems with asynchronous measurement delays and non-logarithmic sensor resolution. A new soft measurement model with asynchronous delays is developed to deal with the inaccurate measurements caused by the non-logarithmic sensor resolution, and a recombination method is proposed to tackle the difficulties induced by the asynchronous measurement delays. The purpose of this paper is to design a finite-horizon filter such that under the joint effects of asynchronous measurement delays and non-logarithmic sensor resolution, an upper bound for the filtering error covariance is ensured and then minimized by appropriately designing the gain parameters. Some sufficient conditions are established to guarantee the boundedness of the proposed filtering algorithm. Finally, two illustrative examples are presented to showcase the effectiveness of the proposed finite-horizon filtering scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"805-820"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716138","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":"Federated Smoothing Proximal Gradient for Quantile Regression With Non-Convex Penalties","authors":"Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner","doi":"10.1109/TSIPN.2025.3587440","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587440","url":null,"abstract":"The rise of internet-of-things (IoT) systems has led to the generation of vast and high-dimensional data across distributed edge devices, often requiring sparse modeling techniques to manage model complexity efficiently. In these environments, quantile regression offers a robust alternative to mean-based models by capturing conditional distributional behavior, which is particularly useful under heavy-tailed noise or heterogeneous data. However, penalized quantile regression in federated learning (FL) remains challenging due to the non-smooth nature of the quantile loss and the non-convex, non-smooth penalties such as MCP and SCAD used for sparsity. To address this gap, we propose the Federated Smoothing Proximal Gradient (FSPG) algorithm, which integrates a smoothing technique into the proximal gradient framework to enable effective, stable, and theoretically guaranteed optimization in decentralized settings. FSPG guarantees monotonic reduction in the objective function and achieves faster convergence than existing methods. We further extend FSPG to handle partial client participation (PCP-FSPG), making the algorithm robust to intermittent node availability by adaptively updating local parameters based on client activity. Extensive experiments validate that FSPG and PCP-FSPG achieve superior accuracy, convergence behavior, and variable selection performance compared to existing baselines, demonstrating their practical utility in real-world federated applications.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"696-710"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680838","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}