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Knowledge graph-enhanced long-tail learning approach for traditional Chinese medicine syndrome differentiation 认领 引用
1
作者 Weikang Kong Chuanbiao Wen Yue Luo 《Digital Chinese Medicine》 CAS CSCD 2026年第1期57-67,共11页
Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning f... Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning framework enhanced by knowledge graphs.Methods We developed Agent-GNN,a three-stage decoupled learning framework,and validated it on the Traditional Chinese Medicine Syndrome Diagnosis(TCM-SD)dataset containing 54152 clinical records across 148 syndrome categories.First,we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system.Second,we proposed a Functional Patient Profiling(FPP)method that utilizes large language models(LLMs)combined with Graph Retrieval-Augmented Generation(RAG)to extract structured symptom-etiology-pathogenesis subgraphs from medical records.Third,we employed heterogeneous graph neural networks to learn structured combination patterns explicitly.We compared our method against multiple baselines including BERT,ZY-BERT,ZY-BERT+Know,GAT,and GPT-4 Few-shot,using macro-F1 score as the primary evaluation metric.Additionally,ablation experiments were conducted to validate the contribution of each key component to model performance.Results Agent-GNN achieved an overall macro-F1 score of 72.4%,representing an 8.7 percentage points improvement over ZY-BERT+Know(63.7%),the strongest baseline among traditional methods.For long-tail syndromes with fewer than 10 samples,Agent-GNN reached a macro-F1 score of 58.6%,compared with 39.3%for ZY-BERT+Know and 41.2%for GPT-4 Few-shot,representing relative improvements of 49.2%and 42.2%,respectively.Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance.Conclusion This study proposes Agent-GNN,a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation.By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs,our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis. 展开更多
关键词 Syndrome differentiation Medical knowledge graph Graph neural networks Long-tail learning Data-efficient learning
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EdgeST-Fusion:A Cross-Modal Federated Learning and Graph Transformer Framework for Multimodal Spatiotemporal Data Analytics in Smart City Consumer Electronics 认领 引用
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作者 Mohammed M.Alenazi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1376-1408,共33页
Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal depend... Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal dependencies,and weak resilience to adversarial updates.To address these limitations,EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics.The architecture integrates cross-modal embedding networks for modality alignment,graph transformer encoders for spatial dependency modeling,temporal self-attention for dynamic pattern learning,and adaptive anomaly detection to ensure data quality and security during aggregation.A privacy-preserving federated learning protocol with differential privacy guarantees enables collaborative model training without centralizing sensitive data.The framework employs data-quality-aware weighted aggregation to enhance robustness against noisy and malicious client updates.Experimental evaluation on the GeoLife,PeMS-Bay,and SmartHome+datasets demonstrates that EdgeST-Fusion achieves 21.8%improvement in prediction accuracy,35.7%reduction in communication overhead,and 29.4%enhancement in security resilience compared to recent baselines.Real-world deployment across three smart city testbeds validates practical viability with 90.0%average accuracy and sub-250 ms inference latency.The proposed framework remains feasible for deployment on heterogeneous and resource-constrained consumer electronics devices whilemaintaining strong privacy guarantees and scalability for large-scale urban environments. 展开更多
关键词 Federated learning graph transformer spatiotemporal analytics consumer electronics smart cities cross-modal fusion edge computing privacy preservation
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Interdisciplinary Arts and Sciences Education:An Exploration on Knowledge Graph-aided I Ching Learning 认领 引用
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作者 Yinuo Liu Wendong Li +3 位作者 Wenxuan Ouyang Gaojie Wang Li Pan Shijun Liu 《计算机教育》 2026年第3期130-138,共9页
To integrate traditional culture and modern technology,Shandong University’s School of Software has promoted an interdisciplinary teaching project called IYAN&ITAN,the I Ching Knowledge Graph.The project,driven b... To integrate traditional culture and modern technology,Shandong University’s School of Software has promoted an interdisciplinary teaching project called IYAN&ITAN,the I Ching Knowledge Graph.The project,driven by I Ching texts,guides students to practice natural language processing(NLP)and knowledge graph technology in a task-oriented curriculum,based on constructivism,situated learning,and inquiry-based pedagogy,with a progressive and task-oriented teaching model.The platform established enables the retrieval of knowledge,parsing of text,symbolic-numeric analysis,and historical commentary integration,making possible multidimensional,structured representation of I Ching knowledge,and offering an extensible reference for interdisciplinary learning in the context of New Engineering Education. 展开更多
关键词 Interdisciplinary teaching Knowledge graph Constructivism Situated learning Inquiry-based learning
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Leveraging Knowledge Graphs and AI to Empower Personalized Learning in English Literature Course 认领 引用
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作者 ZHANG Ruoxi LI Hao +3 位作者 HE Guangshuo WANG Zeyu WU Dingyutong ZHANG Wenwen 《US-China Education Review(A)》 2026年第8期600-608,共9页
Against the backdrop of globalization and global education digitization,traditional English literature instruction for English majors in higher education faces the dual challenges of fragmented knowledge and homogeniz... Against the backdrop of globalization and global education digitization,traditional English literature instruction for English majors in higher education faces the dual challenges of fragmented knowledge and homogenized learning pathways.Grounded in constructivism and adaptive learning theory,this study leverages a smart teaching platform to construct an English literature knowledge graph model encompassing four major genres-novels,poetry,drama,and prose-thereby establishing semantic connections among multidimensional entities such as authors,works,and themes.Empirical research(N=40)indicates that this knowledge graph not only significantly enhances students’cognitive completeness regarding the literary genre system with an overall student satisfaction rate reaching 92.5%,but also effectively stimulates their independent learning and critical thinking.Finally,this paper discusses the challenges regarding the graph’s semantic recognition accuracy and dynamic update mechanisms,and proposes corresponding strategies for optimizing the integration of technology and teaching. 展开更多
关键词 knowledge graph English literature personalized learning ChaoXing platform digital education
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Research on the Construction Method of Famous TCM Physicians’Academic Knowledge Graph Based on Multimodal Deep Learning-Application of the AGBAN Model 认领 引用
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作者 Huisi Hong Yunchou Li +3 位作者 Yiming Yuan Weihan Shen Shiqi Luo Jianlong Huang 《Journal of Clinical and Nursing Research》 2026年第4期416-424,共9页
Aiming at the problems of knowledge fragmentation and opaque reasoning in the digital inheritance of famous TCM physicians’academic thoughts and diagnosis-treatment experience,a multimodal knowledge graph constructio... Aiming at the problems of knowledge fragmentation and opaque reasoning in the digital inheritance of famous TCM physicians’academic thoughts and diagnosis-treatment experience,a multimodal knowledge graph construction method based on the AGBAN model is proposed.Using more than 3,000 outpatient medical records of famous TCM physicians as the data source,multimodal information is integrated to construct a clinical knowledge graph through ontology design,entity-relationship extraction,and knowledge storage.The graph attention network and reinforcement learning mechanism of the AGBAN model are introduced to optimize the diagnosis-treatment path.The results show that the knowledge graph contains 3,089 entities and 1,461 relationships,with an average degree of 2.49;the average reciprocal rank of link prediction of the AGBAN model is 0.973,which is 165.4%higher than that of the TransE model,the diagnosis success rate is 59.19%,and the average reasoning path is 5 steps;cluster analysis verifies the core TCM principles such as“drug-syndrome correspondence”.The conclusion indicates that this method realizes the structured representation and intelligent reasoning of famous TCM physicians’clinical experience,providing a feasible path for TCM academic inheritance and clinical decision support. 展开更多
关键词 Multimodal deep learning Knowledge graph AGBAN model Inheritance of famous TCM physicians’experience Clinical reasoning
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A node importance prediction algorithm based on graph attention and contrastive learning 认领 引用
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作者 Jun Ai Yuming Zhang +2 位作者 Zhan Su Chenye Guo Mingsong Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期671-684,共14页
In complex network analysis,node ranking is vital for propagation prediction,structural optimization,and intervention strategy design,yet existing methods often fail to effectively integrate community information in d... In complex network analysis,node ranking is vital for propagation prediction,structural optimization,and intervention strategy design,yet existing methods often fail to effectively integrate community information in dynamic settings.To address this,this paper proposes a node ranking method that combines graph attention mechanisms with contrastive learning.Community detection is employed to extract node-level community features,and a joint embedding module is designed to fuse global and local structures,thereby incorporating community information into node representations.Based on this,a multi-layer graph attention network adaptively learns node and neighborhood features,while contrastive learning mitigates interference from dynamic evolution and strengthens the model's ability to capture multi-scale structural differences.Experiments on multiple dynamic network datasets show that the proposed method significantly outperforms existing approaches in ranking accuracy,particularly in networks with higher average degrees and clearer community structures.These results validate the effectiveness of the method in enhancing feature representation and modeling multi-scale dynamic node influence. 展开更多
关键词 node importance community features graph attention contrastive learning
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Experience replay with cohesive-subgraph awareness for continual graph learning in IoT 认领 引用
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作者 Zhenzhen Xie Qi Luo +3 位作者 Yan Huang Yongqi Yin Jiaqi Zhang Junjie Pang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期417-427,共11页
In the Internet-of-Things(IoT) scenarios, Continual Graph Learning(CGL) has become a key technique for capturing the complex relational structures underlying diverse applications including sensor networks, road system... In the Internet-of-Things(IoT) scenarios, Continual Graph Learning(CGL) has become a key technique for capturing the complex relational structures underlying diverse applications including sensor networks, road systems and biomedical networks. However, the structural changes in these evolving graphs introduce instability, making catastrophic forgetting a primary challenge for CGL. Experience replay is currently a promising method, as it strikes a balance between new and old knowledge. It also provides CGL models with a human-like memory capability. However, prior work rarely leverages the graph's intrinsic properties to proactively identify the critical patterns hiding in the evolving graphs. To this end, we propose a unified framework that integrates cohesionsubgraph awareness into existing CGL mechanisms. We propose a novel cohesive structure-aware experience replay framework that leverages intrinsic graph properties, such as k-core and k-truss metrics, to guide the selection of representative historical nodes for replay. Unlike conventional replay strategies that rely on random sampling or task-driven node selection, our approach systematically identifies structurally significant nodes that encapsulate the evolving patterns of streaming graphs. By integrating these cohesive subgraph properties into the experience replay process, our method effectively preserves critical historical knowledge while adapting to new graph structures with low computational overhead. The experimental results demonstrate that our method consistently outperforms existing replay strategies in mitigating catastrophic forgetting and maintaining classification performance. On the PubMed dataset, our k-core-based replay strategy improves the F1 score by 3.7% compared to random sampling, while reducing training time by up to 85% compared to full retraining. Similarly, on the Cora dataset, our approach achieves a 98.3% F1 score, surpassing baseline methods by 4.5%. 展开更多
关键词 Graph neural networks Continual learning Experience replay Cohesive subgraph
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Representation Then Augmentation:Wide Graph Clustering Network With Multi-Order Filter Fusion and Double-Level Contrastive Learning 认领 引用
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作者 Youqing Wang Tianxiang Zhao +3 位作者 Mingliang Cui Junbin Gao Li Liang Jipeng Guo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期421-435,共15页
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high c... Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the http://gffzz188fe103f8f1460asqxfn05woqwko6f9p.ffgz.tsg.suse.edu.cn/Tianxiang Zhao0474/WGCN. 展开更多
关键词 Deep graph clustering(DGC) double-level contrastive learning(DCL) multi-order low-pass filter self-supervised representation learning structural consistency
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Neuro-Symbolic Graph Learning for Causal Inference and Continual Learning in Mental-Health Risk Assessment 认领 引用
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作者 Monalisa Jena Noman Khan +1 位作者 Mi Young Lee Seungmin Rho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1311-1338,共28页
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h... Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment. 展开更多
关键词 Catastrophic forgetting causal inference continual learning deep learning graph convolutional network mental health monitoring transformer
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SubPFed:A Personalized Federated Learning Approach with Subgraphs 认领 引用
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作者 Jianbin Li Hang Bao Xin Tong 《Computers, Materials & Continua》 SCIE EI 2026年第7期839-860,共22页
The proliferation of large-scale graph data has enabled Graph Neural Networks (GNNs) to achieve significant success in domains such as recommender systems, social network analysis, and biomedicine. However, in practic... The proliferation of large-scale graph data has enabled Graph Neural Networks (GNNs) to achieve significant success in domains such as recommender systems, social network analysis, and biomedicine. However, in practical networked environments, particularly in distributed service infrastructures, graph data is often isolated between multiple edge smart devices and cannot be shared due to privacy, making GNN models weak in generalization. Subgraph Federated Learning (SFL) mitigates this challenge by treating local client data as subgraphs of the global graph to decentralized GNN training. Unfortunately, client-side missing edges make GNN model difficult to capture dependency information between subgraphs, and local heterogeneous data hinders global model convergence, thereby limiting the performance of federated GNN model. To address this, we propose SubPFed, a personalized federated learning approach tailored for subgraph-based training. SubPFed computes the functional embeddings of local GNNs using random graph inputs. It then estimates subgraph similarity by weighting these embeddings and the structural information of the overlapping nodes. Finally, a personalized weighted aggregation strategy is designed based on the similarity to enhance representation consistency across clients and mitigate data heterogeneity. Experiments on three real-world graph datasets show that SubPFed consistently outperforms state-of-the-art baselines, improving node classification accuracy by 4.28% to 26.50%. Furthermore, SubPFed demonstrates strong robustness under varying subgraph overlap ratios, underscoring its adaptability and scalability in federated graph learning scenarios. 展开更多
关键词 Federated learning graph neural networks node classification
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Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme 认领 引用 被引量：1
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作者 Zhuo Chen Bowen Zhu Chuan Zhou 《Digital Communications and Networks》 SCIE EI CSCD 2025年第1期60-70,共11页
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat... Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods. 展开更多
关键词 Edge computing Network virtualization Container cluster Deep reinforcement learning Graph convolutional network
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Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications 认领 引用
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作者 Wei AN Jiawen LIU +3 位作者 Wenxuan OUYANG Haoyu RU Xuejun LIU Hongqiang LYU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第3期234-248,共15页
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi... With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost. 展开更多
关键词 Computational fluid dynamics High-order discon tinuous Galerkin method Gibbs phenomenon Graph neural networks Zero-shot learning
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A knowledge graph-based reinforcement learning approach for cooperative caching in MEC-enabled heterogeneous networks 认领 引用
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作者 Dan Wang Yalu Bai Bin Song 《Digital Communications and Networks》 SCIE EI CSCD 2025年第4期1236-1244,共9页
Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of conge... Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes.Recently,Multi-access Edge Computing(MEC)-enabled heterogeneous networks,which leverage edge caches for proximity delivery,have emerged as a promising solution to all of these problems.Designing an effective edge caching scheme is critical to its success,however,in the face of limited resources.We propose a novel Knowledge Graph(KG)-based Dueling Deep Q-Network(KG-DDQN)for cooperative caching in MEC-enabled heterogeneous networks.The KGDDQN scheme leverages a KG to uncover video relations,providing valuable insights into user preferences for the caching scheme.Specifically,the KG guides the selection of related videos as caching candidates(i.e.,actions in the DDQN),thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN.Extensive simulation results validate the convergence effectiveness of the KG-DDQN,and it also outperforms baselines regarding cache hit rate and service delay. 展开更多
关键词 Multi-access edge computing Cooperative caching Resource allocation Knowledge graph Reinforcement learning
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Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics 认领 引用
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作者 Mei Ma Wei Ma +2 位作者 Le Gao Zong-Guo Wang Hao Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第1期35-44,共10页
Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experiment... Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experimental trial-and-error processes.However,existing methods,limited by static structural descriptors such as chemical composition and lattice parameters,fail to account for atomic vibrations,which may introduce spurious correlations and undermine predictive reliability.Here,we propose a deep learning model termed integrating graph and dynamical stability(IGDS)for predicting the synthesizability of inorganic crystals.IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties.Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems,achieving precision and recall values of 0.916/0.863 for ternary compounds.By capturing both static structural descriptors and dynamic features,IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals.This approach bridges the gap between theoretical design concepts and their practical implementation,thereby streamlining the development cycle of new materials and enhancing overall research efficiency. 展开更多
关键词 crystal synthesizability prediction deep learning graph learning AI for science
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GFL-SAR: Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement 认领 引用
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作者 Hefei Wang Ruichun Gu +2 位作者 Jingyu Wang Xiaolin Zhang Hui Wei 《Computers, Materials & Continua》 SCIE EI 2026年第1期1683-1702,共20页
Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,exi... Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks. 展开更多
关键词 Graph federated learning GCN GNNs attention mechanism
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NestLipGNN:A Hierarchical Graph Neural Network Framework with Nested Multi-Granularity Learning for Robust Visual Speech Recognition 认领 引用
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作者 Vinh Truong Hoang Nghia Dinh +5 位作者 Luu Quang Phuong Kiet Tran-Trung Ha Duong Thi Hong Bay Nguyen Van Hau Nguyen Trung Thien Ho Huong 《Computers, Materials & Continua》 SCIE EI 2026年第7期1287-1310,共24页
Visual speech recognition(VSR)aims to infer spoken content from visual observations of articulatory movements.Despite significant progress,it remains a challenging task in computer vision and speech processing.Its dif... Visual speech recognition(VSR)aims to infer spoken content from visual observations of articulatory movements.Despite significant progress,it remains a challenging task in computer vision and speech processing.Its difficulty arises from pronounced speaker-to-speaker variability,the presence of homophenes(phonemes that are visually indistinguishable),changes in illumination,and the intrinsically high-dimensional nature of spatiotemporal lip dynamics.In this work,we propose NestLipGNN,a graph-based framework that integrates Graph Neural Networks(GNNs)with a nested multi-granularity learning strategy for visual speech recognition.We construct dynamic lip graphs from facial landmarks to model both spatial relationships between lip regions and their temporal motion during speech articulation.The proposed nested learning architecture supports hierarchical feature extraction across several levels of linguistic abstraction,spanning phoneme-level articulatory units,viseme-level visual speech categories,and word-level semantic representations.We further introduce a Temporal Graph Attention mechanism(T-GAT)that adaptively reweights the importance of distinct lip regions over time.We also introduce a graph-based contrastive learning objective to improve the discrimination of visually similar speech patterns,directly confronting the challenge of homophene resolution.Experiments on the LRW,LRS2,LRS3,and GRID datasets show that NestLipGNN improves recognition accuracy compared with existing methods,obtaining 92.3%word-level accuracy on LRW and delivering a 2.1%absolute performance gain over prior methods.Comprehensive ablation analyses confirm the contribution of each architectural component. 展开更多
关键词 Visual speech recognition graph neural networks nested optimization hierarchical representation learning spatiotemporal modeling contrastive learning lip reading
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Multivariate Data Anomaly Detection Based on Graph Structure Learning 认领 引用
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作者 Haoxiang Wen Zhaoyang Wang +2 位作者 Zhonglin Ye Haixing Zhao Maosong Sun 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1174-1206,共33页
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co... Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment. 展开更多
关键词 Multivariate data anomaly detection graph structure learning coupled network
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Graph and Transformer-Based Deep Learning Paradigms for DDoS Detection:A Systematic and Critical Survey 认领 引用
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作者 Noor Mueen Mohammed Ali Hayder Seyed Amin Hosseini Seno +2 位作者 Mehdi Ebady Manaa Hamid Noori Davood Zabihzadeh 《Computers, Materials & Continua》 SCIE EI 2026年第7期1-43,共43页
With the rapid expansion of networked systems,Distributed Denial-of-Service(DDoS)attacks have become a major threat to Internet security and service availability.Due to their limited scalability,incapacity to capture ... With the rapid expansion of networked systems,Distributed Denial-of-Service(DDoS)attacks have become a major threat to Internet security and service availability.Due to their limited scalability,incapacity to capture temporal and relational relationships,and decreased detection accuracy under dynamic and high-volume network traffic,traditional machine learning algorithms frequently fail in large-scale DDoS scenarios.This encourages the application of deep learning techniques that can simulate intricate relationships.This survey systematically reviews graph-based deep learning and Transformer models for DDoS detection.We categorize methods for transforming network traffic into graph representations and analyze key architectures,including GraphSAGE,GCN,GAT,spatio-temporal Transformers,and hybrid GNN-Transformer models.We summarize the evaluation metrics,datasets,feature extraction strategies,and performance trends reported across existing studies.Results indicate that these approaches effectively capture topological and temporal patterns to detect coordinated attacks.Our comparative review shows that these approaches are capable of capturing both topological and temporal patterns in network traffic,enabling more accurate identification of coordinated DDoS attacks reported in the literature.Remaining challenges include explainability,scalability,data imbalance,and limited generalization.The survey’s contributions are a unified taxonomy,comparative analysis,identification of open challenges,and future research directions toward explainable,lightweight,and federated frameworks. 展开更多
关键词 Distributed Denial-of-Service(DDoS)detection Graph Neural Networks(GNNs) transformer architecture deep learning network security graph-based learning
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Multi-process thermodynamic graph learning for 2D fluid simulation 认领 引用
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作者 Yidi WANG Aik Beng NG +2 位作者 Simon SEE Zhengkui WANG Frank GUAN 《虚拟现实与智能硬件（中英文）》 EI CSCD 2026年第3期330-346,共17页
Solving partial differential equations(PDEs)for fluid simulation is computationally expensive,especially when dealing with complex geometries and high-resolution meshes.Recent advances in physics-informed graph neural... Solving partial differential equations(PDEs)for fluid simulation is computationally expensive,especially when dealing with complex geometries and high-resolution meshes.Recent advances in physics-informed graph neural networks(PIGNNs)have demonstrated potential in approximating such simulations more efficiently.Particularly,thermodynamic informed graph neural networks(TIGNNs)offer a promising data-driven alternative to traditional PDE solvers for fluid simulations.However,existing TIGNN implementations suffer from significant training inefficiencies,requiring prolonged runtimes and high memory consumption due to the need to maintain large parameter matrices in GPU memory.Inspired by multi-processor strategies in deformable solid simulations,we propose a novel multi-processor thermodynamic-informed graph neural network(MP-TIGNN)architecture to significantly accelerate training without compromising accuracy.Our approach enables faster convergence and reduces memory usage with mixed-precision training by leveraging fully sharded data parallelism(FSDP)across multiple GPUs.Experimental results show that our approach reduces training time by approximately 70%compared to the original setup while maintaining similar prediction accuracy. 展开更多
关键词 Fluid simulation Physics informed learning Graph neural network Scientific visualization
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Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models 认领 引用 被引量：1
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作者 Yudong Yan Yinqi Yang +9 位作者 Zhuohao Tong Yu Wang Fan Yang Zupeng Pan Chuan Liu Mingze Bai Yongfang Xie Yuefei Li Kunxian Shu Yinghong Li 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第6期1354-1369,共16页
Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches ofte... Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine. 展开更多
关键词 Drug repurposing Multi-view learning Chemical-induced transcriptional profile Knowledge graph Large language model Heterogeneous network
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