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Dynamic community detection algorithm based on hyperbolic graph convolution

International Journal of Intelligent Computing and Cybernetics 2024年第3期17卷 632-653页

作者： Weijiang Wu Heping Tan Yifeng Zheng College of Information Science and Engineering/College of Artificial Intelligence China University of Petroleum BeijingBeijingChina Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum BeijingBeijingChina School of Computer Science Minnan Normal UniversityZhangzhouChina

Purpose:Community detection is a key factor in analyzing the structural features of complex ***,traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic *** address this challenge,a hyperbolic space-based dynamic graph neural network community detection model(HSDCDM)is ***/methodology/approach:HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincare and Lorentz models to realize feature fusion and information *** addition,the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended ***,the community clustering module divides the community structure by combining the node characteristics of the space domain and the time *** evaluate the performance of HSDCDM,experiments are conducted on both artificial and real ***:Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical *** shows an average improvement of 7.29%in NMI and a 9.07%increase in ARI across datasets compared to traditional *** complex networks with nonEuclidean geometric structures,the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space,provides a more compact embedding that preserves the data structure,and offers advantages over methods based on Euclidean geometry ***/value:This model aggregates the potential information of nodes in space through manifoldpreserving distribution mapping and hyperbolic graph topology ***,it optimizes the Simple Recurrent Unit(SRU)on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space,thereby enhancing computing efficiency by eliminating t

关键词： Complex networks Dynamic community detection Hyperbolic graph neural network

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WPIA:accelerating DNN warm-up in Web browsers by precompiling WebGL programs

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Frontiers of computer science 2024年第6期18卷 281-283页

作者： Deyu TIAN Yun MA Yudong HAN Qi YANG Haochen YANG Gang HUANG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationSchool of Computer SciencePeking UniversityBeijing 100871China Institute for Artificial Intelligence Peking UniversityBeijing 100871China National Key Laboratory of Data Space Technology and System Beijing 100195China

1 *** the advent of artificial intelligence,it is becoming increasingly common to leverage deep learning technologies to provide intelligent services in Web apps[1].Compared with traditional approaches where deep neural network(DNN)runs at cloud servers,DNN inference in Web browsers avoids serverconnections,boostingtheresponsivenesssand enhancing user privacy of Web apps[2].Typically,GPU acceleration is required during DNN inference,especially on enddevices.

关键词： Web browser WebGL

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Bipartite Graph Convolutional Networks with Adversarial Domain Transfer 48

Bipartite Graph Convolutional Networks with Adversarial Doma...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wu, Dong Liang, Bin Liu, Xiangjun Zang, Xuan Chi, Mingmin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center China

ISBN: (纸本)9781728163277

Bipartite graphs have been widely used in many applications such as recommender systems, search engines and so on. Recent works consider bipartite graphs as homogeneous graphs and apply graph convolution networks for link prediction or node classification. However, in bipartite graphs, there are two types of nodes which are from different domains such as users and items in recommender systems, and cannot be in the same embedding space. In this paper, we proposed a novel graph convolution operation to propagate in bipartite graph with less spatial and temporal complexities, and two mapping functions with adversarial constraints to transfer features between two domains. Experimental results show that the proposed model achieves the improved performance on the tasks of link prediction and recommendation in real-world scenarios. © 2023 IEEE.

关键词： Bipartite Graph Neural Networks Collaborative Filtering Domain Transfer

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Bidirectional Identity-Based Inner-Product Functional Re-Encryption in Vaccine data Sharing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 617-628页

作者： Wang, Jing Zhou, Yanwei Zhu, Yasi Liu, Zhiquan Yang, Bo Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education China Jinan University College of Cyber Security Guangzhou510632 China Hubei University of Technology School of Computer Wuhan430068 China

With the development of cloud computing, more and more data is stored in cloud servers, which leads to an increasing degree of privacy of data stored in cloud servers. For example, in the critical domain of medical vaccine trials, where public health outcomes hinge on the analysis of sensitive patient data, the imperative to safeguard privacy has never been more pronounced. Traditional encryption methods, though effective at protecting data, often expose vulnerabilities during decryption and lack the ability to support granular data access and computation. One-way re-encryption schemes further impede the agility of data sharing, which is indispensable for the collaborative efforts of research institutions. To address these limitations, we propose a novel bidirectional re-encryption scheme for inner-product functional encryption (IPFE). Our scheme secures data while allowing computation and sharing in an encrypted state, preserving patient privacy without hindering research. By harnessing inner-product functional encryption, our approach allows authorized researchers to extract valuable insights from encrypted data, significantly enhancing privacy protections. Our scheme's security is predicated on the $l$-ABDHE (augmented bilinear Diffie-Hellman exponent) assumption, ensuring robustness against chosen plaintext attacks within the standard model. This foundation not only secures the data but also yields compact ciphertext length, minimizing storage demands. We introduce a protocol specifically designed for medical vaccine trials, which leverages our bidirectional IB-IPFRE (Identity-Based Inner-Product Functional Re-Encryption) scheme. This protocol enhances data security, supports collaborative research, and maintains patient privacy. Its application in vaccine trials demonstrates the scheme's effectiveness in protecting sensitive information while enabling critical research insights. © 2013 IEEE.

关键词： Encryption Vaccines data Privacy Iron Security Protection Vectors Protocols Cloud Computing Access Control Inner Product Function Encryption Identity Based Encryption CPA Security Bidirectional Re Encryption data Sharing Research Institute Privacy Of data Collaborative Research data Security Sensitive data Privacy Protection Patient Privacy Encrypted data Chosen Plaintext Computational Cost Medical data Vector Of Length Access Control Vector Production Original Information Secret key Public key Security Requirements Bilinear Map Decryption Process key Generation Information Encryption Round Of Experiments Decryption Algorithm Encryption And Decryption Vaccine Information Evaluation Of Drug Efficacy Public Parameters Cryptographic Primitives

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Bootstrap-Based Layerwise Refining for Causal Structure Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2708-2722页

作者： Xiang, Guodu Wang, Hao Yu, Kui Guo, Xianjie Cao, Fuyuan Song, Yukun Hefei University of Technology Key Laboratory of Knowledge Engineering with the Big Data of Ministry of Education Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Shanxi University School of Computer and Information Technology Taiyuan030006 China

Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence (CI) tests to learn a global skeleton layer by layer and then orient the undirected edges to obtain a causal structure. However, the reliability of these statistical tests largely depends on the quality of data samples. In real-life scenarios, the presence of data noise or limited samples often makes many CI tests unreliable at each layer in the skeleton learning phase, leading to an inaccurate skeleton. As the number of layers increases, the inaccurate skeleton will continue to impair the skeleton construction of subsequent layers. Furthermore, an unreliable skeleton hampers the skeleton orientation procedure, resulting in an unsatisfactory causal structure. In this article, we propose a Bootstrap-based layerwise refining (BLR) algorithm for causal structure learning, which includes two new procedures to solve the above problems. First, BLR utilizes a novel layerwise skeleton refining procedure to construct the global skeleton layer by layer based on the bootstrap sampling. Second, BLR employs a collective skeleton orientation procedure that incorporates scoring techniques to collectively orient the global skeleton. The experimental results show that BLR outperforms the state-of-the-art methods on the benchmark Bayesian Network datasets. © 2020 IEEE.

关键词： Refining

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Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Lv, Ying Liu, Zhi Li, Gongyang Chang, Xiaojun Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Engineering and Information Technology University of Technology Sydney Australia

Freespace detection holds a crucial role in autonomous driving technology, particularly in unstructured offroad scenarios that present additional challenges compared to structured road environments. Multimodal fusion methods are widely recognized as effective strategies for addressing these challenges. However, current fusion methods often overlook the critical issue of noise interference in multimodal data. To address this issue, we propose a novel Noise-Aware Intermediary Fusion Network for off-road freespace detection, named NAIFNet. This framework is specifically designed to mitigate noise interference during multimodal fusion. The key component of NAIFNet is the Noise-Aware Intermediary Interaction (NAII) module, which incorporates a denoising template as an intermediary during the fusion process. The NAII module employs multimodal features as query vectors, while the key and value vectors are derived from the search region features of the denoising template. At the same time, noise-aware interaction ensures effective denoising for data in each modality. Furthermore, in the decoding phase, we introduce the Denoising-Guided Decoder (DGD). Leveraging the denoising template, this decoder achieves more precise feature restoration and effectively mitigates the impact of noise. Extensive experiments on the popular benchmark of the offroad freespace detection dataset (ORFD) demonstrate that the proposed NAIFNet achieves state-of-the-art performance. IEEE

关键词： Noise abatement

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Hierarchical Graph Signal Processing for Collaborative Filtering 24

Hierarchical Graph Signal Processing for Collaborative Filte...

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33rd ACM Web Conference, WWW 2024

作者： Xia, Jiafeng Li, Dongsheng Gu, Hansu Lu, Tun Zhang, Peng Shang, Li Gu, Ning Fudan University Shanghai China Microsoft Research Asia Shanghai China Seattle United States School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance Shanghai Institute of Intelligent Electronics and Systems Fudan University China

ISBN: (纸本)9798400701719

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for predicting user future interactions in recommender systems. However, current GSP methods recognize user interaction patterns based on the interactions of all users, so that the recognized interaction patterns are not fully user-matched and easily impacted by other users with different interaction behaviors, resulting in sub-optimal recommendation performance. To this end, we propose a hierarchical graph signal processing method (HiGSP) for collaborative filtering, which consists of two key modules: 1) the cluster-wise filter module that recognizes user unique interaction patterns merely from interactions of users with similar preferences, making the recognized patterns able to reflect user preference without being influenced by other users with different interaction behaviors, and 2) the globally-aware filter module that serves as a complementary to the cluster-wise filter module to recognize user general interaction patterns more effectively from all user interactions. By linearly combining these two modules, HiGSP can recognize user-matched interaction patterns, so as to model user preference and predict user future interactions more accurately. Extensive experiments on six real-world datasets demonstrate the superiority of HiGSP compared to other GCN-based and GSP-based recommendation methods in terms of efficacy and efficiency. © 2024 ACM.

关键词： Collaborative filtering

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Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization 41

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Sto...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Zhuanghua Chen, Cheng Luo, Luo Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Singapore CNRS@CREATE LTD 1 Create Way #08-01 CREATE Tower 138602 Singapore Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Data Science Fudan University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics China

This paper studies the problem of solving nonconvex nonsmooth optimization over a closed convex set. Most previous works tackle such problems by transforming the constrained problem into an unconstrained problem. However, they only provide asymptotic convergence analysis for their methods. In this work, we provide the non-asymptotic convergence analysis for solving constrained nonconvex nonsmooth optimization. We first generalize classical gradient mapping and the Frank-Wolfe gap in the nonsmooth setting. Then we introduce novel notions of approximate stationarity concerning such generalized quantities. We also propose several stochastic zeroth-order algorithms for the problem, along with their non-asymptotic convergence guarantees of obtaining the proposed approximate stationarity. Finally, we conduct numerical experiments that demonstrate the effectiveness of our algorithms. Copyright 2024 by the author(s)

关键词： Optimization algorithms

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