检索结果-内蒙古大学图书馆

HRMG-EA: Heterogeneous graph neural network recommendation with multi-level guidance based on enhanced-attributes

Applied Intelligence 2025年第6期55卷 1-22页

作者： Wang, Longtao Yuan, Guiyuan Li, Chao Zhao, Yufei Duan, Hua Zeng, Qingtian College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic and Information Engineering Shandong University of Science and Technology Shandong Qingdao266590 China College of Mathematics and Systems Science Shandong University of Science and Technology Shandong Qingdao266590 China

Heterogeneous Graph Neural Networks are an efficient and powerful tool for modeling graph structure data in recommendation systems. However, existing heterogeneous graph neural networks often fail to model the dependencies between user and item attribute preferences, limiting graph structure optimization and consequently reducing the accuracy of recommendations. To overcome these issues, we propose a Heterogeneous graph neural network Recommendation with Multi-level Guidance based on Enhanced-Attributes (HRMG-EA). First, we design an attribute enhanced gated network to model user-item interaction attribute scenarios and obtain enhanced-attributes by capturing complex attribute dependencies. It effectively avoids the expansion of the graph scale in attribute graph scenarios and further covers personalized attribute relationship distribution characteristics of users and items. Then, we propose a novel multi-level graph structure guidance strategy based on enhanced-attributes. It guides graph structure learning from three optimization levels, optimizing from two perspectives: explicit (heterogeneity and homogeneity) and implicit (contrast enhancement). The former can screen higher-quality heterogeneous neighbor nodes in a direct interaction environment, and filter out redundant or erroneous edges under different similar semantic interest paths to improve the quality of the neighborhood environment. The latter aligns representation embeddings of enhanced-attributes and graph structure in a latent space, explores their potential commonalities, and obtains more comprehensive, fine-grained semantic and beneficial structural information. Finally, on two real-world datasets, HRMG-EA significantly outperforms the state-of-the-art baseline algorithms in both recall and normalized discounted cumulative gain. A large number of ablation experiments and analytical verifications also verify its effectiveness. © The Author(s), under exclusive licence to Springer science+Business Med

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Review of Deep Learning-Based Entity Alignment Methods 1

引用

18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Lu, Dan Han, Guoyu Zhao, Yingnan Han, Qilong College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

Entity alignment aims to discover different references to the same entity in different graphs, and it is a key technique for solving graph-related problems. It has developed into one of the important tasks in knowledge graphs and has received extensive attention from scholars in recent years. Through entity alignment, data from multiple isolated knowledge graphs with different sources and modes can be summarized and classified, forming a more information-rich knowledge base. In early research on entity alignment, researchers first proposed a class of alignment methods based on knowledge representation learning and verified that these methods have significant improvements over traditional methods. However, entity alignment still has many defects and challenges to be addressed, such as a lack of scalability, differences in language and relationship type definitions in knowledge graphs from different sources, which make entity alignment difficult. There are also problems such as the need to improve the quality of large-scale knowledge graph data and optimize computational efficiency. Thus, it is difficult to perform entity alignment on multiple knowledge graphs through simple translation and transformation. This paper discusses the deep learning-based methods that have emerged in the field of entity alignment based on the definition of entity alignment and data sets as standards, summarizes the shortcomings and limitations of these methods, and introduces commonly used data sets in the entity alignment task. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

引用

Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

引用

computer Modeling in Engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

来源：评论

学校读者我要写书评

暂无评论

Plasma-assisted fabrication of multiscale materials for electrochemical energy conversion and storage

引用

Carbon Energy 2025年第2期7卷 144-171页

作者： Chen Li Tengfei Zhang Zhong Qiu Beirong Ye Xinqi Liang Xin Liu Minghua Chen Xinhui Xia Chen Wang Wangjun Wan Yongqi Zhang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina Key Laboratory of Engineering Dielectric and Applications(Ministry of Education) School of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbinChina State Key Laboratory of New Textile Materials and Advanced Processing Technology Wuhan Textile UniversityWuhanChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhouChina School of Materials Science and Engineering Zhejiang UniversityHangzhouChina Zhejiang Academy of Science and Technology for Inspection and Quarantine HangzhouChina

Plasma,the fourth state of matter,is characterized by the presence of charged particles,including ions and *** has been shown to induce unique physical and chemical ***,there have been increased applications of plasma technology in the field of multiscale functional materials'preparation,with a number of interesting *** review will begin by introducing the basic knowledge of plasma,including the definition,typical parameters,and classification of plasma *** this,we will provide a comprehensive review and summary of the applications(phase conversion,doping,deposition,etching,exfoliation,and surface treatment)of plasma in common energy conversion and storage systems,such as electrocatalytic conversion of small molecules,batteries,fuel cells,and *** article summarizes the structure-performance relationships of electrochemical energy conversion and storage materials(ECSMs)that have been prepared or modified by *** also provides an overview of the challenges and perspectives of plasma technology,which could lead to a new approach for designing and modifying electrode materials in ECSMs.

关键词： electrocatalysts energy conversion and storage nanomaterials plasma

来源：评论

学校读者我要写书评

暂无评论

Graph self-supervised long-tail item augmentation for recommendation

引用

Neural Computing and Applications 2025年 1-23页

作者： Wen, Xilin Yang, Xu-Hua Long, Hai-Xia College of Computer Science and Technology Zhejiang University of Technology Zhejiang310023 China

Self-supervised learning empowers models to learn from unlabeled data, making it a popular technique for addressing the challenge of insufficient labeled data. In this paper, we combine the issue of cold items lacking supervision signals due to the long-tail distribution in recommendation systems, which can lead to model degradation, and propose graph self-supervised long-tail item augmentation for recommendation (SLIA). We study the proxy task of distinguishing between popular items and cold items and employ various graph structure perturbation schemes on the user–item interaction graph (U–I graph) for contrastive learning. To be more specific, we propose a proportional edge dropping strategy for popular items. For cold items, while retaining their sparse interaction data, we introduce random perturbations to create a new enhanced U–I graph. Through the fine-grained construction of contrastive views, this scheme ensures a global perturbation of the original views and prevents the loss of information for cold items in the tail of the power-law distribution. Additionally, we propose a contrastive loss with a punishment mechanism to explicitly enhance the invariance of node representations. Through theoretical analysis, we find that SLIA is not only effective in mining hard negative samples but also reduces the occurrence of outliers in the representation distribution space. Experimental results on four benchmark datasets demonstrate that SLIA achieves competitive results in both supervised and self-supervised recommendation algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Multiple object tracking based on appearance and motion graph convolutional neural networks with an explainer

引用

Neural Computing and Applications 2024年第22期36卷 13799-13814页

作者： Zhang, Yubo Huang, Qingming Zheng, Liying School of Computer Science and Technology Harbin Engineering University 145 Nantong Street Heilongjiang Harbin150001 China School of Computer Science and Technology University of Chinese Academy of Sciences Huairou District Beijing100019 China

The tracking performance of Multi-Object Tracking (MOT) has recently been improved by using discriminative appearance and motion features. However, dense crowds and occlusions significantly reduce the reliability of these features, resulting in unsatisfied tracking performance. Thus, we design an end-to-end MOT model based on Graph Convolutional Neural Networks (GCNNs) which fuses four classes of features that characterize objects from their appearances, motions, appearance interactions, and motion interactions. Specifically, a Re-Identification (Re-ID) module is used to extract more discriminative appearance features. The appearance features from object tracklets are then averaged to simplify the proposed tracker. Then, we design two GCNNs to better distinguish objects. One is for extracting interactive appearance features, and the other is for interactive motion features. A fusion module then fuses those features, getting the global feature similarity based on which an association component calculates the MOT matching results. Finally, we semantically visualize relevant structures with the GNNExplainer for insight into the proposed tracker. The evaluation results on MOT16 and MOT17 benchmarks show that our model outperforms the state-of-the-art online tracking methods in terms of Multi-Object Tracking Accuracy and Identification F1 score which is consistent with the results from the GNNExplainer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Loop Closure Detection via Locality Preserving Matching With Global Consensus

引用

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 411-426页

作者： Jiayi Ma Kaining Zhang Junjun Jiang IEEE the Electronic Information School Wuhan UniversityWuhan 430072China the School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited ***,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D *** this work,a novel appearance-based LCD system is ***,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD *** is demonstrated that this setting is memory-wise efficient and can achieve remarkable *** dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive ***,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few *** performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging *** show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD *** have released our code of LPM-GC at https://***/jiayi-ma/LPM-GC.

关键词： Feature matching locality preserving matching loop closure detection SLAM

来源：评论

学校读者我要写书评

暂无评论

Underwater Biological Target Detection Algorithm and Research Based on YOLOv7 Algorithm

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第6期51卷 594-601页

作者： Zhuang, Hongwei Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1× 1 convolutional architecture are added to the DCN module to improve the model’s perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Modifying Lyapunov exponent of chaotic map by self-cascading

引用

science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun School of Electronic and Information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：