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Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks

IEEE ACCESS 2025年 13卷 9290-9302页

作者： Furutani, Satoshi Aoshima, Tatsuhiro Shibahara, Toshiki Akiyama, Mitsuaki Aida, Masaki NTT Social Informat Labs Musashino Tokyo 1808585 Japan Tokyo Metropolitan Univ Grad Sch Syst Design Hino Tokyo 1910065 Japan

Viral marketing, a marketing strategy that utilizes word-of-mouth (WOM), is effective in increasing brand awareness and acquiring new customers, as WOM allows information to reach a large audience in social networks. In the past two decades, for efficient viral marketing, many studies on maximizing advertising reach, known as influence maximization, have been conducted in the field of data mining. However, most of them ignore the possibility of the emergence of negative opinions in the information diffusion process. In general, negative opinions are more contagious than positive ones, and ignoring them may even lead undesirable outcomes, such as a decline in brand image and a decrease in purchases. To address this issue, we consider the problem of suppressing the negative influence that emerges endogenously on social networks through preemptive node interventions, such as persuasion, nudging, or warnings. Namely, given a limited budget for interventions, who should be targeted to efficiently suppress the spread of negative opinions in the social network? We formulate this problem as a combinatorial optimization problem on graphs. We prove that this problem is NP-hard and propose approximation algorithms to identify optimal intervention nodes that minimize the negative influence. Through numerical experiments, we demonstrate that our algorithms effectively suppress the negative influence regardless of the type of social network or experimental setting.

关键词： Integrated circuit modeling approximation algorithms Diffusion models Minimization Probabilistic logic Optimization Ions Heuristic algorithms Companies Influence maximization information diffusion social networks social networks

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Dynamic Weighted Energy Minimization for Aerial Edge Computing Networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 683-697页

作者： Li, Yihang Gao, Xiaozheng Shi, Minwei Kang, Jiawen Niyato, Dusit Yang, Kai Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

In this article, we develop a dynamic weighting strategy which considers the residual energy of different devices in aerial edge computing networks, and formulate a weighted energy consumption optimization problem aimed at extending device operating duration. To solve the formulated problem, we develop a clustering algorithm using K-means++ to establish optimal user-to-unmanned-aerial-vehicle access relationships, and the optimization problem is decomposed into trajectory, transmit power, and bandwidth subproblems. Each subproblem is sequentially solved by using the successive convex approximation algorithm, and the entire optimization problem is resolved by using the block coordinate descent algorithm. Simulation results demonstrate the effectiveness of our proposed weighting strategy in managing the energy levels of users, which prolongs the operational duration of the devices.

关键词： Optimization Autonomous aerial vehicles Energy consumption Heuristic algorithms Clustering algorithms Bandwidth approximation algorithms Costs Vehicle dynamics Time division multiple access Aerial edge computing network (AECN) dynamic weighting strategy mobile edge computing (MEC) trajectory optimization weighted energy minimization

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Soft-Output Successive Cancellation List Decoding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第2期71卷 1007-1017页

作者： Yuan, Peihong Duffy, Ken R. Medard, Muriel MIT Network Coding & Reliable Commun Grp Cambridge MA 02139 USA Northeastern Univ Engn Probabil Informat & Commun Lab Boston MA 02115 USA

We introduce an algorithm for approximating the codebook probability that is compatible with all successive cancellation (SC)-based decoding algorithms, including SC list (SCL) decoding. This approximation is based on an auxiliary distribution that mimics the dynamics of decoding algorithms with an SC decoding schedule. Based on this codebook probability and SCL decoding, we introduce soft-output SCL (SO-SCL) to generate both blockwise and bitwise soft-output (SO). Using that blockwise SO, we first establish that, in terms of both block error rate (BLER) and undetected error rate (UER), SO-SCL decoding of dynamic Reed-Muller (RM) codes significantly outperforms the CRC-concatenated polar codes from 5G New Radio under SCL decoding. Moreover, using SO-SCL, the decoding misdetection rate (MDR) can be constrained to not exceed any predefined value, making it suitable for practical systems. Proposed bitwise SO can be readily generated from blockwise SO via a weighted sum of beliefs that includes a term where SO is weighted by the codebook probability, resulting in a soft-input soft-output (SISO) decoder. Simulation results for SO-SCL iterative decoding of product codes and generalized LDPC (GLDPC) codes, along with information-theoretical analysis, demonstrate significant superiority over existing list-max and list-sum approximations.

关键词： Maximum likelihood decoding Codes Polar codes Iterative decoding approximation algorithms Heuristic algorithms Error analysis Vectors Encoding Convolutional codes Polar coding codebook probability generalized decoding joint error correction and detection soft-input soft-output (SISO) decoding

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Zeroth-Order Learning in Continuous Games via Residual Pseudogradient Estimates

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2258-2273页

作者： Huang, Yuanhanqing Hu, Jianghai Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

A variety of practical problems can be modeled by the decision-making process in multiplayer games where a group of self-interested players aim at optimizing their own local objectives, while the objectives depend on the actions taken by others. The local gradient information of each player, essential in implementing algorithms for finding game solutions, is all too often unavailable. In this article, we focus on designing solution algorithms for multiplayer games using bandit feedback, i.e., the only available feedback at each player's disposal is the realized objective values. To tackle the issue of large variances in the existing bandit learning algorithms with a single oracle call, we propose two algorithms by integrating the residual feedback scheme into single-call extragradient methods. Subsequently, we show that the actual sequences of play can converge almost surely to a critical point if the game is pseudomonotone plus and characterize the convergence rate to the critical point when the game is strongly pseudomonotone. The ergodic convergence rates of the generated sequences in monotone games are also investigated as a supplement. Finally, the validity of the proposed algorithms is further verified via numerical examples.

关键词： Games Convergence Optimization Mirrors approximation algorithms Linear programming Heuristic algorithms Vectors Decision making Wireless communication Bandit methods game theory iterative learning control multi-agent systems optimization methods variational inequality

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Tensor Networks for Latent Variable Analysis: Novel algorithms for Tensor Train approximation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第11期31卷 4622-4636页

作者： Phan, Anh-Huy Cichocki, Andrzej Uschmajew, Andre Tichavsky, Petr Luta, George Mandic, Danilo P. Skolkovo Inst Sci & Technol Skoltech Ctr Computat & Data Intens Sci & Engn Moscow 143026 Russia Skolkovo Inst Sci & Technol Skoltech Moscow 143026 Russia Tokyo Univ Agr & Technol Tokyo 1838538 Japan Hangzhou Dianzi Univ Coll Comp Sci Hangzhou 310018 Peoples R China Syst Res Inst PL-01447 Warsav Poland Max Planck Inst Math Sci D-04103 Leipzig Germany Czech Acad Sci Inst Informat Theory & Automat Prague 18200 Czech Republic Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20057 USA Imperial Coll Signal Proc London SW7 2AZ England

Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model that represents data as an ordered network of subtensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network (TN) decomposition has been long studied in quantum physics and scientific computing. In this article, we present novel algorithms and applications of TN decompositions, with a particular focus on the tensor train (TT) decomposition and its variants. The novel algorithms developed for the TT decomposition update, in an alternating way, one or several core tensors at each iteration and exhibit enhanced mathematical tractability and scalability for large-scale data tensors. For rigor, the cases of the given ranks, given approximation error, and the given error bound are all considered. The proposed algorithms provide well-balanced TT-decompositions and are tested in the classic paradigms of blind source separation from a single mixture, denoising, and feature extraction, achieving superior performance over the widely used truncated algorithms for TT decomposition.

关键词： Tensors approximation algorithms Signal processing algorithms Matrix decomposition Signal processing Data models approximation error Blind source separation image denoising nested Tucker tensor network (TN) tensor train (TT) decomposition tensorization Tucker-2 (TK2) decomposition truncated singular value decomposition (SVD)

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More on the complexity of defensive domination in graphs

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DISCRETE APPLIED MATHEMATICS 2025年 362卷 167-179页

作者： Henning, Michael A. Pandey, Arti Tripathi, Vikash Indian Inst Technol Ropar Dept Math Rupnagar 140001 Punjab India Indian Inst Technol Mandi Sch Math & Stat Sci Mandi 175005 Himachal Prades India Univ Johannesburg Dept Math & Appl Math ZA-2006 Auckland Pk South Africa

In a graph G = (V, E), a non-empty set A of k distinct vertices, is called a k-attack on G. The vertices in the set A are considered to be under attack. A set D subset of V can defend or counter the attack A on G if there exists a one-to-one function f : A bar right arrow D, such that either f (u) = u or there is an edge between u, and its image f (u), in G. A set D is called a k-defensive dominating set if it defends against any k-attack on G. Given a graph G = (V, E), the minimum k-defensive domination problem requires us to compute a minimum cardinality k-defensive dominating set of G. When k is not fixed, it is co-NP-hard to decide if D subset of V is a k-defensive dominating set. However, when k is fixed, the decision version of the problem is NP-complete for general graphs. On the positive side, the problem can be solved in linear time when restricted to paths, cycles, co-chain, and threshold graphs for any k. This paper mainly focuses on the problem when k > 0 is fixed. We prove that the decision version of the problem remains NPcomplete for bipartite graphs;this answers a question asked by Ekim et al. (Discrete Math. 343 (2) (2020)). We establish a lower and upper bound on the approximation ratio for the problem. Further, we show that the minimum k-defensive domination problem is APX-complete for bounded degree graphs. On the positive side, we show that the problem is efficiently solvable for complete bipartite graphs for any k > 0. Towards the end, we study a relationship between the defensive domination number and another well-studied domination parameter. (c) 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Domination Defensive domination NP-completeness Graph algorithms approximation algorithms APX-completeness

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Research on Traffic Dynamic Shortest Path Allocation Model Based on Triangular Fuzzy Number Weight and K-Means Algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Sun, Hanzheng Li, Dan Zhi, Baoping Ren, Yongming Cheng, Xingyan Yellow River Conservancy Tech Inst Sch Civil Engn & Transportat Engn Kaifeng 475004 Peoples R China

Aiming at the traffic dynamic shortest path allocation problem with triangular fuzzy numbers as attribute values, a traffic dynamic shortest path allocation model method based on triangular fuzzy number weights is proposed. The weighted distance optimization model of triangular fuzzy number, ideal solution and negative ideal solution is established. The weight value of each attribute is obtained by solving and optimizing. The similarity difference between attribute information is determined longitudinally by using the idea of deviation maximization, and the evaluation uncertainty of each scheme under different attributes is described horizontally by entropy value. The attribute weights based on reliability are obtained by synthesizing the similarity difference index and the uncertainty index. Based on triangular fuzzy weights. A subset of vertex set V, R, is selected as the representative vertex, and the shortest path distance between all vertex pairs in R is calculated. At the same time, considering the spatial correlation of road traffic flow and the dynamic change process of traffic flow in each section of the road network, the time is discretized, and the abrupt point of road traffic state is taken as the node pair, and the road traffic flow is dynamically loaded. The prediction model of road section travel time under occasional congestion is established. Experiments show that when the error upper limit is small, the retrieval results of the approximation algorithm are relatively accurate. Then, an example analysis of traffic dynamic path allocation is provided, which shows the effectiveness and feasibility of this method in traffic dynamic shortest path allocation.

关键词： Heuristic algorithms Decision making Roads Dynamic scheduling Resource management Complexity theory Uncertainty Shortest path problem approximation algorithms Planning Triangular fuzzy number weight K-means algorithm traffic dynamic shortest path traffic flow

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Robust Beamforming Design for IOS-Assisted Multiuser MISO Systems With Imperfect CSI

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 808-821页

作者： Yao, Xinyi Hu, Fengye Ling, Zhuang Zhang, Hongliang Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China Peking Univ Sch Elect Beijing 100871 Peoples R China

Intelligent omni-surface (IOS) has been identified as an innovative technology to achieve omnidirectional wireless coverage for mobile users. However, due to the passive characteristics of the IOS, accurate channel state information (CSI) is difficult to acquire in IOS-assisted communication systems. In this article, we investigate a novel IOS-assisted multiuser multiple-input-single-output (MISO) downlink communication system. Specifically, the cascaded channel errors on both sides of the IOS are modeled separately to improve the flexibility and stability of the robust beamforming schemes. Considering the diverse practical communication requirements posed by the bounded and statistical CSI error models, we formulated the system sum-rate maximization and transmission power minimization problems for the worst-case and outage-constrained robust beamforming, respectively. S -Procedure and Bernstein-type inequality are introduced to approximate the original nonconvex problems. Finally, we decompose the transformed problem into two subproblems and present an alternate optimization (AO) algorithm based on the success convex approximation (SCA) technique and the branch and bound method. Simulation results demonstrate that our robust beamforming schemes can effectively mitigate the system performance degradation caused by CSI error and enhance the downlink transmission robustness of the IOS-assisted communication system.

关键词： Array signal processing Communication systems Optimization MISO communication approximation algorithms Wireless communication Numerical models Heuristic algorithms Downlink Vehicle dynamics Imperfect channel state information (CSI) intelligent omni-surface (IOS) phase shift design robust beamforming optimization simultaneously transmitting and reflecting

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Sustainable Placement With Cost Minimization in Wireless Digital Twin Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1064-1077页

作者： Zhou, Yuzhi Fu, Yaru Shi, Zheng Hung, Kevin Quek, Tony Q. S. Zhang, Yan Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong 999077 Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 519070 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore Univ Oslo Dept Informat N-0313 Oslo Norway

Digital twin (DT) technology has a high potential to satisfy different requirements of the ever-expanding new applications. Nonetheless, the DT placement in wireless digital twin networks (WDTNs) poses a significant challenge due to the conflict between unpredictable workloads and the limited capacity of edge servers. In other words, each edge server has a risk of overload when handling an excessive number of tasks or services. Overload risks can have detrimental effects on a network's sustainability, yet this aspect is often overlooked in the literature. In this paper, we aim to study the sustainability-aware DT placement problem for WDTNs from a cost minimization perspective. To this end, we formulate the DT placement-driven cost optimization problem as a chance-constrained integer programming problem. For tractability, we transform the original non-deterministic problem into a deterministic integer linear programming (ILP) problem using the sample average approximation (SAA) approach. We prove that the transformed problem remains NP-hard and thus finding a global optimal solution is very difficult. To strike a balance between time efficiency and performance guarantee, we propose an improved local search algorithm for this ILP by identifying high-quality starting states from historical search data and enhancing the search process. Numerical results show a lower cost and higher efficiency of our proposed method compared with the previous schemes.

关键词： Sustainable development Servers Costs Optimization approximation algorithms Wireless communication Quality of service Cost minimization digital twin placement sample average approximation sustainability control time efficient algorithm

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Real-Time 5.7-5.8 GHz 32-Beam Approximate Discrete Fourier Transform Spectrum Sensor for RF Perception on Xilinx Sx475T

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Madanayake, Arjuna Kumarasiri, Umesha Sivasankar, Sivakumar Lawrance, Keththura Gayanath, Buddhipriya Silva, Hiruni Mandal, Soumyajit Cintra, Renato J. Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA Brookhaven Natl Lab Dept Elect & Comp Engn New York NY 11973 USA UFPE Dept Technol Signal Proc Grp BR-55014900 Caruaru Brazil

The radio spectrum in the sub-6 GHz (FR1) band is crowded and contested, and is sought after by commercial, scientific and defense users. Situational awareness through spectrum sensing, and AI/ML-enabled perception that recognizes behaviors, patterns, modulations, devices and waveforms is a crucial need for emerging autonomous/cognitive radio systems. This work describes measurable progress in the use of extremely low complexity approximate DFT algorithms as multi-beam beamformers in the digital domain for multibeam spatial RF beamforming. The paper begins with a longterm vision for intelligent spectrum awareness across wide bands and multi-directions with multi-chiplet system in package hardware acceleration of both beamforming, Fourier and AI/ML algorithms, followed by a focus account of specific progress with digital architectures and real-time prototype implementations across the 5.7-5.8 GHz band for 32 RF beams. A real-time temporal frequency resolution of 100 kHz across 100 MHz of baseband bandwidth is achieved, across 32 simultaneous fully-digital RF-beams, using a Xilinx Sx475 FPGA implementation. Details of multiplierless approximate DFT beamformers, automated modulation recognition algorithms using AI/ML, analog channelization, spectrum sensing and perception architectures are also discussed. Over-the-air experiments using the RadioML.2018.a dataset confirmed both single source accuracy (better than 97%) and impact of multi-beams on AI/ML performance for multiple strong RFI sources.

关键词： Discrete Fourier transforms Radio frequency Complexity theory Real-time systems Sensors Array signal processing approximation algorithms Sparse matrices Field programmable gate arrays Fast Fourier transforms Spectrum sensing FFT radio frequency wireless AI approximation Fourier integrated circuits FPGA chiplets

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