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Stochastic Block Models for Complex Network Analysis: A Survey

ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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Efficient Implementation of Aperture Fill Time Correction for Wideband Array Using the Low-complexity Keystone Transform

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Progress In Electromagnetics Research C 2023年 136卷 101-112页

作者： Wang, Lin Jiang, Yiyang Jiang, Yu Tian, Baoli Shen, Mingwei College of Computer and Information Engineering Hohai University Nanjing China Unit 95438 of PLA Air Force Meishan China Science and Technology on Electronic Information Control Laboratory Chengdu China

In order to remove the influence of the aperture fill time (AFT) for wideband array, the scaling principle of the Keystone (KT) transform is applied to eliminate the linear coupling between spatial domain and frequency domain of wideband array signal. However, the classic KT transform is implemented by interpolation Sinc which is difficult to apply in engineering and leads to the serious problem of insufficient data. To address this, a realization of the low-complexity KT transform is presented, and it is implemented using only the Chirp-z transform (CZT) and fast Fourier transforms (FFT). Additionally, an Autoregressive (AR) model is proposed to compensate the insufficient data for each range, and the order of AR is estimated by the rank of the signal covariance matrix. Simulation results demonstrate that the proposed algorithm significantly reduces computational burden and improves the performance of wideband array beamforming. © 2023, Electromagnetics Academy. All rights reserved.

关键词： Fast Fourier transforms

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Suppressing high voltage chemo-mechanical degradation in single crystal nickel-rich cathodes for high-performance all-solid-state lithium batteries

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Journal of Energy Chemistry 2025年第3期102卷 377-385页

作者： Yirong Xiao Le Yang Chaoyuan Zeng Ze Hua Shuangquan Qu Niaz Ahmad Ruiwen Shao Wen Yang Key Laboratory of Cluster Science of Ministry of Education Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion MaterialsSchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Medical TechnologyBeijing Institute of TechnologyBeijing 100081China School of Chemistry and Chemical Engineering Key Laboratory of Ministry of Education for Advanced Materials in Tropical Island ResourcesCollaborative Innovation Center of Ecological CivilizationHainan UniversityHaikou 570228HainanChina

Sulfide-based all-solid-state lithium batteries suffer from electrochemo-mechanical damage to Ni-rich oxide-based cathode active materials(CAMs),primarily caused by severe volume changes,results in significant stress and strain,causes micro-cracks and interfacial contact loss at potentials>4.3 V(***/Li^(+)).Quantifying micro-cracks and voids in CAMs can reveal the degradation mechanisms of Ni-rich oxidebased cathodes during electrochemical ***,the origin of electrochemical-mechanical damage remains ***,We have developed a multifunctional PEG-based soft buffer layer(SBL)on the surface of carbon black(CB).This layer functions as a percolation network in the single crystal LiNi_(0.83)Co_(0.07)Mn_(0.1)O_(2)and Li_(6)PS_(5)Cl composite cathode layer,ensuring superior ionic conductivity,reducing void formation and particle cracking,and promoting uniform utilization of the cathode active material in all-solid-state lithium batteries(ASSLBs).High-angle annular dark-field STEM combined with nanoscale X-ray holo-tomography and plasma-focused ion beam scanning electron microscopy confirmed that the PEG-based SBL mitigated strain induced by reaction heterogeneity in the *** strain produces lattice stretches,distortions,and curved transition metal oxide layers near the surface,contributing to structural degradation at elevated ***,ASSLBs with a LiNi_(0.83)Co_(0.07)Mn_(0.1)O_(2)cathode containing LCCB-10(CB/PEG mass ratio:100/10)demonstrate a high areal capacity(2.53 mAh g^(-1)/0.32 mA g^(-1))and remarkable rate capability(0.58 mAh g^(-1)at 1.4 mA g^(-1)),with88%capacity retention over 1000 cycles.

关键词： Single crystal nickel-rich oxide cathode Lattice stretches and distortions Reaction heterogeneity Percolation network All-solid-state lithium batteries

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Sensitivity Improvements for Picosecond Ultrasonic Thickness Measurements in Gold and Tungsten Nanoscale Films

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Nanomanufacturing and Metrology 2024年第2期7卷 1-12页

作者： Jiaqi Dong Chengyuan Yao Yuanhao Zhu Shaojie Li Bowen Liu JinTao Fan Chunguang Hu Youjian Song Minglie Hu Key Laboratory of Opto-Electronic Information Science and Technology of Ministry of Education School of Precision Instruments and Opto-Electronics EngineeringTianjin UniversityTianjin 300072China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin UniversityTianjin 300072China

Picosecond ultrasonics,as a nondestructive and noncontact method,can be employed for nanoscale metallic film thickness *** sensitivity of the system,which determines the measurement precision and practicability of this technique,is often limited by the weak intensity of the ultrasonic *** solve this problem,we investigate the distinct mechanisms involved in picosecond ultrasonic thickness measurement for two types of metals,namely tungsten(W)and gold(Au).For thickness measurement in W films,theory and simulation show that optimizing the pump and probe laser wavelengths,which determine the intensity and shape of the ultrasonic signal,is critical to improving measurement sensitivity,while for Au film measurements,where acoustic-induced beam distortion is dominant,the signal intensity can be optimized by selecting an appropriate aperture size and sample *** above approaches are validated in experiments.A dual-wavelength pump-probe system is constructed based on a passively mode-locked ytterbium-doped fiber *** smoothing method and multipeak Gaussian fitting are employed for the extraction of ultrasonic *** measurement precision is achieved in a series of W and Au films with thicknesses of 43-750 *** work can be applied to various high-precision,noncontact measurements of metal film thickness in the semiconductor industry.

关键词： Picosecond ultrasonics Thickness measurement Optical pump-probe Beam distortion technique

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Towards Gradient Equalization and Feature Diversification for Long-Tailed Multi-Label Image Recognition

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IEEE Transactions on Multimedia 2025年 27卷 3489-3500页

作者： Chen, Zhao-Min Cui, Quan Zhang, Xiaoqin Deng, Ruoxi Xia, Chaoqun Lu, Shijian Nanjing University National Key Laboratory for Novel Software Technology Nanjing210093 China Waseda University Graduate School of Information Production and Systems Fukuoka169-8050 Japan Wenzhou University College of Computer Science and Artificial Intelligence Wenzhou325060 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem, i.e., head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted;2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group- wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals. © 1999-2012 IEEE.

关键词： Labeled data

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Self organizing optimization and phase transition in reinforcement learning minority game system

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Frontiers of physics 2024年第4期19卷 297-309页

作者： Si-Ping Zhang Jia-Qi Dong Hui-Yu Zhang Yi-Xuan Lü Jue Wang Zi-Gang Huang Key Laboratory of Biomedical Information Engineering of Ministry of Education Key Laboratory of Neuro-informatics&Rehabilitation Engineering of Ministry of Civil Affairsand Institute of Health and Rehabilitation ScienceSchool of Life Science and TechnologyXi’an Jiaotong UniversityXi’an 710049China Lanzhou Center for Theoretical Physics and Key Laboratory of Theoretical Physics of Gansu Province Lanzhou UniversityLanzhou 730000China

Whether the complex game system composed of a large number of artificial intelligence(AI)agents empowered with reinforcement learning can produce extremely favorable collective behaviors just through the way of agent self-exploration is a matter of practical *** this paper,we address this question by combining the typical theoretical model of resource allocation system,the minority game model,with reinforcement *** individual participating in the game is set to have a certain degree of intelligence based on reinforcement learning *** particular,we demonstrate that as AI agents gradually becomes familiar with the unknown environment and tries to provide optimal actions to maximize payoff,the whole system continues to approach the optimal state under certain parameter combinations,herding is effectively suppressed by an oscillating collective behavior which is a self-organizing pattern without any external *** interesting phenomenon is that a first-order phase transition is revealed based on some numerical results in our multi-agents system with reinforcement *** order to further understand the dynamic behavior of agent learning,we define and analyze the conversion path of belief mode,and find that the self-organizing condensation of belief modes appeared for the given trial and error rates in the AI ***,we provide a detection method for period-two oscillation collective pattern emergence based on the Kullback–Leibler divergence and give the parameter position where the period-two appears.

关键词： oscillatory evolution collective behaviors phase transition reinforcement learning minority game

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem 17

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Xu, Jiahua Xie, Ningyi Lee, Xinwei Saito, Yoshiyuki Asai, Nobuyoshi Cai, Dongsheng University of Tsukuba Graduate School of Science and Technology Japan Singapore Management University School of Computing and Information Systems Singapore University of Aizu Graduate School of Computer Science and Engineering Japan University of Aizu School of Computer Science and Engineering Japan University of Tsukuba Faculty of Engineering Information and Systems Japan

ISBN: (纸本)9798331530471

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart. © 2024 IEEE.

关键词： Vehicle routing

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Nonconvex Noise-Tolerant Neural Model for Repetitive Motion of Omnidirectional Mobile Manipulators

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1766-1768页

作者： Zhongbo Sun Shijun Tang Jiliang Zhang Junzhi Yu the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE College of Information Science and Engineering Northeastern UniversityShenyang 110819China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

Dear Editor,Quadratic programming problems(QPs)receive a lot of attention in various fields of science computing and engineering applications,such as manipulator control[1].Recursive neural network(RNN)is considered t... 详细信息

关键词： directional programming hardware

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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