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Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning

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arXiv 2023年

作者： Li, Gaichao Chen, Jinsong Hopcroft, John E. He, Kun Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Hopcroft Center on Computing Science Huazhong University of Science and Technology Wuhan China Department of Computer Science Cornell University Ithaca United States

Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation. An important line called node dropping pooling aims at exploiting learnable scoring functions to drop nodes with comparatively lower significance scores. However, existing node dropping methods suffer from two limitations: (1) for each pooled node, these models struggle to capture long-range dependencies since they mainly take GNNs as the backbones;(2) pooling only the highest-scoring nodes tends to preserve similar nodes, thus discarding the affluent information of low-scoring nodes. To address these issues, we propose a Graph Transformer Pooling method termed GTPool, which introduces Transformer to node dropping pooling to efficiently capture long-range pairwise interactions and meanwhile sample nodes diversely. Specifically, we design a scoring module based on the self-attention mechanism that takes both global context and local context into consideration, measuring the importance of nodes more comprehensively. GTPool further utilizes a diversified sampling method named Roulette Wheel Sampling (RWS) that is able to flexibly preserve nodes across different scoring intervals instead of only higher scoring nodes. In this way, GTPool could effectively obtain long-range information and select more representative nodes. Extensive experiments on 11 benchmark datasets demonstrate the superiority of GTPool over existing popular graph pooling methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning

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arXiv 2023年

作者： Chen, Jinsong Li, Gaichao Hopcroft, John E. He, Kun Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Hopcroft Center on Computing Science Huazhong University of Science and Technology Wuhan China Department of Computer Science Cornell University Ithaca United States

The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention mechanism, the core module of graph Transformers, as a two-step aggregation operation on a fully connected graph. Due to the property of generating positive attention values, the self-attention mechanism is equal to conducting a smooth operation on all nodes, preserving the low-frequency information. However, only capturing the low-frequency information is inefficient in learning complex relations of nodes on diverse graphs, such as heterophily graphs where the high-frequency information is crucial. To this end, we propose a Signed Attention-based Graph Transformer (SignGT) to adaptively capture various frequency information from the graphs. Specifically, SignGT develops a new signed self-attention mechanism (SignSA) that produces signed attention values according to the semantic relevance of node pairs. Hence, the diverse frequency information between different node pairs could be carefully preserved. Besides, SignGT proposes a structure-aware feed-forward network (SFFN) that introduces the neighborhood bias to preserve the local topology information. In this way, SignGT could learn informative node representations from both long-range dependencies and local topology information. Extensive empirical results on both node-level and graph-level tasks indicate the superiority of SignGT against state-of-the-art graph Transformers as well as advanced GNNs. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Quantum Hamiltonian Descent for Non-smooth Optimization

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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

A Deep Penetration Network for Sentence Classification

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SSRN

SSRN 2023年

作者： Chen, Yanping Zhong, Xinyang Liu, Yijun Dong, Bo Zheng, Qinghua The College of Computer Science and Technology The Engineering Research Center of Text Computing and Cognitive Intelligence Lab Guizhou University Guizhou Guiyang550025 China Department of Computer Science and Technology Xi’an Jiaotong University Shanxi Xi’an710049 China

Sentence classification is an important task in natural language processing. In deep architectures, the task suffers from a serious semantic vanishing problem when stacking a large number of networks. To address this problem, the current methods try to enclose a mass of features with different granularities in a deep network. The main problem the methods face is that the silent feature representations are easily weakened and mixed with noisy information, which is not effective in learning contextual features and constructing semantic dependencies in a sentence. In this paper, a deep penetration network (DPN) is designed to improve deep architectures' ability to preserve the favourable semantic features. The DPN enables salient features to penetrate through deeper architectures and to construct long semantic dependencies between them. This approach is evaluated on seven public datasets. Our experiments show that the DPN exhibits a stable performance with deeper architectures. It improves the performance on three types of sentence classification tasks, outperforming the existing state-of-the-art models. © 2023, The Authors. All rights reserved.

关键词： Semantics

A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

Understanding Wireless Charger Networks: Concepts, Current Research, and Future Directions

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IEEE Communications Surveys and Tutorials 2024年

作者： Ren, Meixuan Dai, Haipeng Liu, Tang Deng, Xianjun Dou, Wanchun Yang, Yuanyuan Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Stony Brook University Department of Electrical and Computer Engineering NY11794 United States

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs. © 1998-2012 IEEE.

关键词： Inductive power transmission

An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia computer science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

GSLB: The Graph Structure Learning Benchmark 37

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GSLB: The Graph Structure Learning Benchmark

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

Deep Learning-based Sentiment Classification: A Comparative Survey

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arXiv 2023年

作者： Kayed, Mohamed Díaz Redondo, Rebeca P. Mabrouk, Alhassan Computer Science Department Faculty of Computers and Artificial Intelligence Beni-Suef University Egypt Information & Computing Lab AtlanTTIC Research Center Telecommunication Engineering School Universidade de Vigo Spain Math. and Computer Science Department Faculty of Science Beni-Suef University Egypt

Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis