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SIRQU: Dynamic Quarantine Defense Model for Online Rumor Propagation Control

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第6期9卷 1703-1714页

作者： Liu, Zhaoli Qin, Tao Sun, Qindong Li, Shancang Song, Houbing Herbert Chen, Zhouguo Xian Univ Technol Sch Comp Sci & Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ MOE Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China Univ West England UWE Bristol Dept Comp Sci & Creat Technol Bristol BS16 1QY Avon England Embry Riddle Aeronaut Univ Secur & Optimizat Networked Globe Lab SONG Lab Daytona Beach FL 32114 USA China Elect Technol Grp Corp Res Inst 30 Chengdu 610093 Peoples R China

Rumors can spread very rapidly through online social networks (OSNs), leading to huge negative impact on human society. Hence, there is an urgent need to develop models that can minimize the spread of rumors. In this article, we propose a novel framework to improve the cost and efficiency of rumor propagation control. First, to reduce the impact of rumor controlling mechanism on users' normal activities, we introduce a soft dynamic quarantine strategy into rumor propagation control and develop a new propagation model named susceptible-infected-removed-quarantined ignorants-quarantined spreaders (SIRQU) to model and block the rumor propagation in the network. Second, to further improve the control efficiency, we propose an influential node selection algorithm based on discrete particle swarm optimization with an evolutionary search strategy, and the controlling mechanism is only applied on the most influential nodes. Finally, we conduct a series of simulations and experiments on several public datasets and the dataset collected from Sina Weibo to validate the proposed method, and the results show that the proposed method outperfoms the related baseline algorithms.

关键词： Heuristic algorithms Social networking (online) Infectious diseases Computational modeling approximation algorithms Epidemics Costs Dynamic quarantine defense influential node identification online social network (OSN) management rumor propagation control

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Sampling With Replacement vs Poisson Sampling: A Comparative Study in Optimal Subsampling

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第10期68卷 6605-6630页

作者： Wang, Jing Zou, Jiahui Wang, HaiYing Univ Connecticut Dept Stat Storrs CT 06269 USA Capital Univ Econ & Business Sch Stat Beijing 100070 Peoples R China

Faced with massive data, subsampling is a commonly used technique to improve computational efficiency, and using nonuniform subsampling probabilities is an effective approach to improve estimation efficiency. For computational efficiency, subsampling is often implemented with replacement or through Poisson subsampling. However, no rigorous investigation has been performed to study the difference between the two subsampling procedures such as their estimation efficiency and computational convenience. This paper performs a comparative study on these two different sampling procedures. In the context of maximizing a general target function, we first derive asymptotic distributions for estimators obtained from the two sampling procedures. The results show that the Poisson subsampling may have a higher estimation efficiency. Based on the asymptotic distributions for both subsampling with replacement and Poisson subsampling, we derive optimal subsampling probabilities that minimize the variance functions of the subsampling estimators. These subsampling probabilities further reveal the similarities and differences between subsampling with replacement and Poisson subsampling. The theoretical characterizations and comparisons on the two subsampling procedures provide guidance to select a more appropriate subsampling approach in practice. Furthermore, practically implementable algorithms are proposed based on the optimal structural results, which are evaluated through both theoretical and empirical analyses.

关键词： Estimation Probability Computational efficiency approximation algorithms Distributed databases Convergence Training Algorithmic sampling asymptotic distribution informative sample massive data

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Graph Matching Via the Lens of Supermodularity

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第5期34卷 2200-2211页

作者： Konar, Aritra Sidiropoulos, Nicholas D. Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

Graph matching, the problem of aligning a pair of graphs so as to minimize their edge disagreements, has received widespread attention owing to its broad spectrum of applications in data science. As the problem is NP-hard in the worst-case, a variety of approximation algorithms have been proposed for obtaining high quality, suboptimal solutions. In this article, we approach the task of designing an efficient polynomial-time approximation algorithm for graph matching from a previously unconsidered perspective. Our key result is that graph matching can be formulated as maximizing a monotone, supermodular set function subject to matroid intersection constraints. We leverage this fact to apply a discrete optimization variant of the minorization-maximization algorithm which exploits supermodularity of the objective function to iteratively construct and maximize a sequence of global lower bounds on the objective. At each step, we solve a maximum weight matching problem in a bipartite graph. Differing from prior approaches, the algorithm exploits the combinatorial structure inherent in the problem to generate a sequence of iterates featuring monotonically non-decreasing objective value while always adhering to the combinatorial matching constraints. Experiments on real-world data demonstrate the empirical effectiveness of the algorithm relative to the prevailing state-of-the-art.

关键词： Linear programming Optimization approximation algorithms Bipartite graph Indexes Proteins approximation methods Graph matching discrete optimization supermodularity matroid intersection minorization-maximization weighted bipartite matching

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Event-Triggered ADP for Nonzero-Sum Games of Unknown Nonlinear Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第5期33卷 1905-1913页

作者： Zhao, Qingtao Sun, Jian Wang, Gang Chen, Jie Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Chongqing Innovat Ctr Beijing Inst Technol Chongqing 401120 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

For nonzero-sum (NZS) games of nonlinear systems, reinforcement learning (RL) or adaptive dynamic programming (ADP) has shown its capability of approximating the desired index performance and the optimal input policy iteratively. In this article, an event-triggered ADP is proposed for NZS games of continuous-time nonlinear systems with completely unknown system dynamics. To achieve the Nash equilibrium solution approximately, the critic neural networks and actor neural networks are utilized to estimate the value functions and the control policies, respectively. Compared with the traditional time-triggered mechanism, the proposed algorithm updates the neural network weights as well as the inputs of players only when a state-based event-triggered condition is violated. It is shown that the system stability and the weights' convergence are still guaranteed under mild assumptions, while occupation of communication and computation resources is considerably reduced. Meanwhile, the infamous Zeno behavior is excluded by proving the existence of a minimum inter-event time (MIET) to ensure the feasibility of the closed-loop event-triggered continuous-time system. Finally, a numerical example is simulated to illustrate the effectiveness of the proposed approach.

关键词： Games Neural networks Optimal control Nonlinear dynamical systems approximation algorithms Nash equilibrium Heuristic algorithms Adaptive dynamic programming (ADP) event-triggered nonzero-sum (NZS) games reinforcement learning (RL)

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The Stochastic Boolean Function Evaluation problem for symmetric Boolean functions

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DISCRETE APPLIED MATHEMATICS 2022年 309卷 269-277页

作者： Gkenosis, Dimitrios Grammel, Nathaniel Hellerstein, Lisa Kletenik, Devorah Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Greece Univ Maryland Dept Comp Sci 8125 Paint Branch Dr College Pk MD 20742 USA NYU Dept Comp Sci & Engn 370 Jay St Brooklyn NY 11201 USA CUNY Brooklyn Coll Dept Comp & Informat Sci 2900 Bedford Ave Brooklyn NY 11210 USA

We give two approximation algorithms solving the Stochastic Boolean Function Evaluation (SBFE) problem for symmetric Boolean functions. The first is an O(log n) approximation algorithm, based on the submodular goal-value approach of Deshpande, Hellerstein and Kletenik. Our second algorithm, which is simple, is based on the algorithm solving the SBFE problem for k-of -n functions, due to Salloum, Breuer, and Ben-Dov. It achieves a (B - 1) approximation factor, where B is the number of blocks of 0's and 1's in the standard vector representation of the symmetric Boolean function. As part of the design of the first algorithm, we prove that the goal value of any symmetric Boolean function is less than n(n + 1)/2. Finally, we give an example showing that for symmetric Boolean functions, minimum expected verification cost and minimum expected evaluation cost are not necessarily equal. This contrasts with a previous result, given by Das, Jafarpour, Orlitsky, Pan and Suresh, which showed that equality holds in the unit-cost case. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Submodularity Boolean functions Sequential testing approximation algorithms

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Burning Graphs Through Farthest-First Traversal

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IEEE ACCESS 2022年 10卷 30395-30404页

作者： Garcia-Diaz, Jesus Cesar Perez-Sansalvador, Julio Xibai Rodriguez-Henriquez, Lil Maria Alejandro Cornejo-Acosta, Jose Consejo Nacl Ciencia & Technol Mexico City 03940 DF Mexico Inst Nacl Astrofis Opt & Electr Coordinat Ciencias Computacionales Puebla 72840 Mexico

Graph burning is a process to determine the spreading of information in a graph. If a sequence of vertices burns all the vertices of a graph by following the graph burning process, then such a sequence is known as a burning sequence. The graph burning problem consists in finding a minimum length burning sequence for a given graph. The solution to this NP-hard combinatorial optimization problem helps quantify a graph's vulnerability to contagion. This paper introduces a simple farthest-first traversal-based approximation algorithm for this problem over arbitrary graphs. We refer to this proposal as the Burning Farthest-First (BFF) algorithm. BFF runs in O(n(3)) steps and has a tight approximation factor of 3 - 2/b(G), where b(G) is the size of an optimal solution. The main attribute of BFF is that it has a better approximation factor than the state-of-the-art approximation algorithms for arbitrary graphs, which report an approximation factor of 3. Despite being simple, BFF proved practical when tested over some benchmark datasets.

关键词： approximation algorithms farthest-first traversal graph burning information spreading social contagion

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HARS: A High-Available and Resource-Saving Service Function Chain Placement Approach in Data Center Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第2期19卷 829-847页

作者： Niu, Meng Han, Qingmian Cheng, Bo Wang, Meng Xu, Ziqi Gu, Wenyuan Zhang, Shuhao Chen, Junliang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100000 Peoples R China China Elect Technol Grp Corp Res Inst 54 Shijiazhuang 050002 Hebei Peoples R China

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200%. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75% of the resources to achieve the same availability as the state-of-the-art models.

关键词： Switches Heuristic algorithms Data centers Costs Servers Reliability approximation algorithms Network function virtualization service function chain availability backup resources allocation

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Policy Gradient for Continuing Tasks in Discounted Markov Decision Processes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第9期67卷 4467-4482页

作者： Paternain, Santiago Bazerque, Juan Andres Ribeiro, Alejandro Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA Univ Republica Dept Elect Engn Montevideo Uruguay Univ Pittsburgh Pittsburgh PA 15260 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

Reinforcement learning aims to find policies that maximize an expected cumulative reward in Markov decision processes with unknown transition probabilities. Policy gradient (PG)-algorithms use stochastic gradients of the value function to update the policy. A major drawback of PG-algorithms is that they are limited to episodic tasks (multiple finite-horizon trajectories) unless stringent stationarity assumptions are imposed on the trajectories. Hence, they need restarts and cannot be fully implemented online, which is critical for systems need to adapt to new tasks and/or environments in deployment. Moreover, the standard stationary formulation ignores transient behaviors. This motivates our study of discounted MDPs of infinite horizon without restarts. However, it is unknown if in this case following stochastic PG-type estimates would improve the policy. The main result of this work is to establish that when policies belong to a reproducing kernel Hilbert space (RKHS), and the kernel is selected properly, then these PG-estimates are ascent directions for the value function conditioned to any arbitrary initial point. This allows us to prove convergence of our online algorithm to the local optima. A numerical example shows that an agent running our online algorithm learns to navigate and succeeds in a surveillance task that requires looping between two goal locations. This example corroborates our theoretical findings about the ascent directions of subsequent stochastic gradients. It also shows how our online algorithm guides the agent through a continuing cyclic trajectory that does not comply with the standard stationarity assumptions in the literature for non-episodic training.

关键词： Task analysis Trajectory Convergence Markov processes approximation algorithms Transient analysis Standards Adaptive systems gradient methods reinforcement learning stochastic systems

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Sample-Based and Feature-Based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3832-3847页

作者： Cui, Ying Li, Yangchen Ye, Chencheng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Federated learning (FL) has become a hot research area in enabling the collaborative training of machine learning models among multiple clients that hold sensitive local data. Nevertheless, unconstrained federated optimization has been studied mainly using stochastic gradient descent (SGD), which may converge slowly, and constrained federated optimization, which is more challenging, has not been investigated so far. This paper investigates sample-based and feature-based federated optimization, respectively, and considers both unconstrained and constrained nonconvex problems for each of them. First, we propose FL algorithms using stochastic successive convex approximation (SSCA) and mini-batch techniques. These algorithms can adequately exploit the structures of the objective and constraint functions and incrementally utilize samples. We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively. Next, we provide algorithm examples with appealing computational complexity and communication load per communication round. We show that the proposed algorithm examples for unconstrained federated optimization are identical to FL algorithms via momentum SGD and provide an analytical connection between SSCA and momentum SGD. Finally, numerical experiments demonstrate the inherent advantages of the proposed algorithms in convergence speeds, communication and computation costs, and model specifications.

关键词： Optimization Signal processing algorithms Stochastic processes Privacy approximation algorithms Servers Computational modeling Federated learning nonconvex optimization stochastic optimization stochastic successive convex approximation

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Design of Probabilistic Shaping 4D Ultra High Order Modulation Format With 8APSK Pilot Aided Carrier Phase Recovery

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第12期40卷 3688-3697页

作者： Tian, Feng Wu, Tianze Ji, Yiqing Wang, Chuxuan Zhang, Qi Gao, Ran Li, Zhipei Tian, Qinghua Xin, Xiangjun Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

A new four-dimensional (4D) probabilistically shaped (PS) ultra-high order modulation format is proposed with pilot aided carrier phase recovery (PA-CPR) algorithm to improve the general mutual information (GMI) performance. While the structure of the 4D-PS ultra-high order modulation format uses constellation set partitioning (SP) based on amplitude translation, and the PA-CPR applies 8-level amplitude phase shift keying (8APSK) pilot. To demonstrate the feasibility of the scheme, the PS-1024QAM system is designed on the simulation platform. The results show that the proposed 8APSK-PA-CPR algorithm outperforms the QPSK-PA-CPR algorithm. The gain of the optimal 8APSK-PA-CPR is similar to 0.4 dB compared with the quadrature phase-shift keying (QPSK) PA-CPR algorithm. The steep drop of blind phase search (BPS) with different PS factors is overcome by 8APSK-PA-CPR when the signal-to-noise (SNR) ratio is lower than 24 dB. The maximum improvement of GMI is 1.3 bit/symbol when the SNR is higher than 26 dB and the complexity of the 8APSK-PA-CPR algorithm is reduced by 99.9% compared with the BPS algorithm. The gain obtained from the proposed 4D modulation scheme is similar to 2.8 dB under the condition of GMI = 17.7971 bit/symbol. It demonstrated that this scheme has a strong tolerance to noise, low computational complexity, and high coding gain. It is a feasible and flexible scheme for PS high-order modulation format transmission systems.

关键词： Partitioning algorithms Modulation Probabilistic logic Phase shift keying Signal to noise ratio approximation algorithms Gain Carrier phase recovery coherent optical communications four-dimensional pilot aided probabilistic shaping set partitioning

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