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

{"title":"Online Auditing of Information Flow","authors":"Mor Oren-Loberman;Vered Azar;Wasim Huleihel","doi":"10.1109/TSIPN.2024.3399558","DOIUrl":"10.1109/TSIPN.2024.3399558","url":null,"abstract":"Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society. In this paper, we consider the problem of online auditing of information flow/propagation with the goal of classifying news items as fake or genuine. Specifically, driven by experiential studies on real-world social media platforms, we propose a probabilistic Markovian information spread model over networks modeled by graphs. We then formulate our inference task as a certain sequential detection problem with the goal of minimizing the combination of the error probability and the time it takes to achieve the correct decision. For this model, we find the optimal detection algorithm minimizing the aforementioned risk and prove several statistical guarantees. We then test our algorithm over real-world datasets. To that end, we first construct an offline algorithm for learning the probabilistic information spreading model, and then apply our optimal detection algorithm. Experimental study show that our algorithm outperforms state-of-the-art misinformation detection algorithms in terms of accuracy and detection time.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"487-499"},"PeriodicalIF":3.2,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937171","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":"Distributed Generalized Nash Equilibria Computation of Noncooperative Games Via Novel Primal-Dual Splitting Algorithms","authors":"Liang Ran;Huaqing Li;Lifeng Zheng;Jun Li;Zhe Li;Jinhui Hu","doi":"10.1109/TSIPN.2024.3364613","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3364613","url":null,"abstract":"This article investigates the generalized Nash equilibria (GNE) seeking problem for noncooperative games, where all players dedicate to selfishly minimizing their own cost functions subject to local constraints and coupled constraints. To tackle the considered problem, we initially form an explicit local equilibrium condition for its variational formulation. By employing proximal splitting operators, a novel distributed primal-dual splitting algorithm with full-decision information (Dist_PDS_FuDeIn) is designed, eliminating the need for global step-sizes. Furthermore, to address scenarios where players lack access to all other players' decisions, a local estimation is introduced to approximate the decision information of other players, and a fully distributed primal-dual splitting algorithm with partial-decision information (Dist_PDS_PaDeIn) is then proposed. Both algorithms enable the derivation of new distributed forward-backward-like extensions. Theoretically, a new analytical approach for convergence is presented, demonstrating that the proposed algorithms converge to the variational GNE of games, and their convergence rates are also proven, provided that uncoordinated step-sizes are positive and less than explicit upper bounds. Moreover, the approach not only generalizes the forward-backward splitting technique but also improves convergence rates of several well-known algorithms. Finally, the advantages of Dist_PDS_FuDeIn and Dist_PDS_PaDeIn are illustrated through comparative simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"179-194"},"PeriodicalIF":3.2,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942779","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":"Strong Convergence of a Random Actions Model in Opinion Dynamics","authors":"Olle Abrahamsson;Danyo Danev;Erik G. Larsson","doi":"10.1109/TSIPN.2024.3361373","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3361373","url":null,"abstract":"We study an opinion dynamics model in which each agent takes a random Bernoulli distributed action whose probability is updated at each discrete time step, and we prove that this model converges almost surely to consensus. We also provide a detailed critique of a claimed proof of this result in the literature. We generalize the result by proving that the assumption of irreducibility in the original model is not necessary. Furthermore, we prove as a corollary of the generalized result that the almost sure convergence to consensus holds also in the presence of a stubborn agent which never changes its opinion. In addition, we show that the model, in both the original and generalized cases, converges to consensus also in \u0000<inline-formula><tex-math>$r$</tex-math></inline-formula>\u0000th mean.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"147-161"},"PeriodicalIF":3.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139739018","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":"Online Signed Sampling of Bandlimited Graph Signals","authors":"Wenwei Liu;Hui Feng;Feng Ji;Bo Hu","doi":"10.1109/TSIPN.2024.3356794","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3356794","url":null,"abstract":"The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as “like” or “dislike” for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated. The online signed sampling problem of a graph signal can be formulated as a Markov decision process in a finite horizon. Unfortunately, it is intractable for large size graphs. We propose a low-complexity greedy signed sampling algorithm (GSS) as well as a stopping criterion. Meanwhile, we prove that the objective function is adaptive monotonic and adaptive submodular, so that the performance is close enough to the global optimum with a lower bound. Finally, we demonstrate the effectiveness of the GSS algorithm by both synthesis and realworld data.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"131-146"},"PeriodicalIF":3.2,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732034","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":"Protocol-Based Distributed Security Fusion Estimation for Time-Varying Uncertain Systems Over Sensor Networks: Tackling DoS Attacks","authors":"Lijuan Zha;Yaping Guo;Jinliang Liu;Xiangpeng Xie;Engang Tian","doi":"10.1109/TSIPN.2024.3356789","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3356789","url":null,"abstract":"This article studies the distributed fusion estimation (DFE) issue for networked multi-sensor systems (NMSSs) with stochastic uncertainties, bandwidth-constrained network and energy-constrained denial-of-service (DoS) attacks. The stochastic uncertainties reflected in both the state and measurement models are characterized by multiplicative noises. For reducing the communication burden, local estimation signals are subject to dimensionality reduction processing. And the improved Round-Robin (RR) protocol is used on the channels from local estimators to the fusion estimator. To reflect the actual situation, the dimensionality reduction strategy is designed from the defender's point of view in the sense of minimum fusion error covariance (FEC). And the attack strategy is designed from the attacker's point of view in the sense of maximum FEC. Then, based on a compensation model, a recursive distributed Kalman fusion estimation algorithm (DKFEA) is proposed. The stability conditions making the mean square error (MSE) for DFE bounded are derived. In the end, the validity of the presented DKFEA is verified by an illustrative example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"119-130"},"PeriodicalIF":3.2,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695114","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":"Lyapunov-Optimized and Energy-Constrained Stable Online Computation Offloading in Wireless Microtremor Sensor Networks","authors":"Ruyun Tian;Hongyan Xing;Yihan Cao;Huaizhou Zhang","doi":"10.1109/TSIPN.2024.3355748","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3355748","url":null,"abstract":"The microtremor survey method (MSM) holds great potential for obtaining subsurface shear wave velocity structures in exploration geophysics. However, the lack of an instant imaging mechanism with local fast computation and processing has become a significant bottleneck hindering the development of MSM. In instant imaging tasks, the computational resources of ordinary nodes employed for imaging are often limited. In this article, we consider a single-point microtremor array network with time-varying wireless channels and stochastic imaging task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability and optimize service quality subject to the long-term data queue stability and average power constraints. We formulate the problem as a the minimum delay problem that jointly determines the binary offloading and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework named LyECCO that combines the Lyapunov optimization and energy consumption optimization, solve the binary offloading problems with very low computational complexity. Simulation results show the feasibility of the LyECCO, which achieves optimal computation performance while stabilizing all queues in the system.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"83-93"},"PeriodicalIF":3.2,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654439","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":"Hybrid-Triggered Output Feedback Containment Control for Multi-Agent Systems With Missing Measurements","authors":"Arumugam Parivallal;Sangwoon Yun;Yoon Mo Jung","doi":"10.1109/TSIPN.2024.3355747","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3355747","url":null,"abstract":"In this paper, we investigate the output feedback containment control problem for multi-agent systems with missing measurements. The primary objective is to design a hybrid-triggered controller that not only reduces the unwanted data transmission but also ensures the required control performance. The proposed hybrid-triggered controller is developed by combining the time-triggered and event-triggered schemes using a Bernoulli random variable. Utilizing Lyapunov stability theory, we derive sufficient conditions to ensure the containment control of the considered multi-agent system. Finally, we verify the derived theoretical results through two numerical examples.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"108-118"},"PeriodicalIF":3.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694911","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":"Detection and Recovery of Hidden Submatrices","authors":"Marom Dadon;Wasim Huleihel;Tamir Bendory","doi":"10.1109/TSIPN.2024.3352264","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352264","url":null,"abstract":"In this paper, we study the problems of detection and recovery of hidden submatrices with elevated means inside a large Gaussian random matrix. We consider two different structures for the planted submatrices. In the first model, the planted matrices are disjoint, and their row and column indices can be arbitrary. Inspired by scientific applications, the second model restricts the row and column indices to be consecutive. In the detection problem, under the null hypothesis, the observed matrix is a realization of independent and identically distributed standard normal entries. Under the alternative, there exists a set of hidden submatrices with elevated means inside the same standard normal matrix. Recovery refers to the task of locating the hidden submatrices. For both problems, and for both models, we characterize the statistical and computational barriers by deriving information-theoretic lower bounds, designing and analyzing algorithms matching those bounds, and proving computational lower bounds based on the low-degree polynomials conjecture. In particular, we show that the space of the model parameters (i.e., number of planted submatrices, their dimensions, and elevated mean) can be partitioned into three regions: the \u0000<italic>impossible</i>\u0000 regime, where all algorithms fail; the \u0000<italic>hard</i>\u0000 regime, where while detection or recovery are statistically possible, we give some evidence that polynomial-time algorithm do not exist; and finally the \u0000<italic>easy</i>\u0000 regime, where polynomial-time algorithms exist.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"69-82"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572632","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 Multi-View Rumor Detection Framework Using Dynamic Propagation Structure, Interaction Network, and Content","authors":"Marzieh Rahimi;Mehdy Roayaei","doi":"10.1109/TSIPN.2024.3352267","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352267","url":null,"abstract":"Social networks (SN) have been one of the most important media for information diffusion in recent years. However, sometimes SN are used to spread rumors, which results in many social issues. Many researches have been done to detect rumors automatically. Previous works mostly exploit a single modality, especially the textual content, thus ignoring other modality such as the propagation structure and the interaction network of the rumor. However, the interaction network of users and tweets, and the propagation structure of a tweet, can provide important information to be used in rumor detection. In this paper, we propose a multi-view rumor detection framework (MV-RD) which captures multiple views of a tweet simultaneously including propagation structure, interaction network, and content. Previous works that considered propagation structure, mostly have used the final propagation structure at the end of information diffusion. Few related researchers have considered the dynamic evolution of propagation structures. In this paper, using partitioning of propagation structure over time, we have designed a propagation structure model that learns the evolution of the propagation structure of rumors over time. Besides, we take advantage of features of the rumor interaction network (modeling first-level interactions of tweets) for detecting rumors using the interaction network model. Also, a content model is learned to detect rumors using the tweet contents. Finally, these three models are fused into a unified framework. The results show the effectiveness of using multiple views in the rumor detection task. The proposed framework can detect rumors more effectively than other existing methods, even without using the tweet content. The proposed method achieved accuracies of 77.82%, 85.65%, and 88.26% by leveraging the propagation structure model alone, combining the propagation structure and interaction network models, and incorporating all three models, respectively. These results outperformed previous approaches and also demonstrated the method's capability to detect rumors earlier than existing methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"48-58"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504532","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 Graph-Assisted Framework for Multiple Graph Learning","authors":"Xiang Zhang;Qiao Wang","doi":"10.1109/TSIPN.2024.3352236","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352236","url":null,"abstract":"In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting the underlying topological relationships between them. The difficulty lies in how to design a regularizer that accurately describes the intricate topological relationships, especially without prior knowledge. This problem becomes more challenging for the scenarios where data for different graphs are stored separately and prohibited from being transmitted to an unreliable central server due to privacy concerns. To address these issues, we propose a novel regularizer termed pattern graph to flexibly describe our priors on topological patterns. Theoretically, we provide the estimation error upper bound of the proposed graph estimator, which characterizes the impact of some factors on estimation errors. Furthermore, an approach that can automatically discover relationships among graphs is proposed to handle awkward situations without priors. On the algorithmic aspect, we develop a decentralized algorithm that updates each graph locally without sending the private data to a central server. Finally, extensive experiments on both synthetic and real data are carried out to validate the proposed method, and the results demonstrate that our framework outperforms the state-of-the-art methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"162-178"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738933","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}