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Modularized Graph Convolutional Network 认领 引用
1
作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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Voxel event graph neural network for event-based human gait recognition 认领 引用
2
作者 Guangyuan MA Xiaolin GONG +2 位作者 Jiangtao XU Jiandong GAO Zhaoxuan GUO 《Optoelectronics Letters》 EI 2026年第3期167-173,共7页
To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the gr... To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the graph.Then the edges are constructed based on spatio-temporal distance and temporal order constraints so that the event stream is constructed as a graph structure.Finally,a lightweight feature extraction network based on graph neural networks(GNNs)is used to efficiently capture spatio-temporal information and motion cues from the event graph.To evaluate our method,an event-based gait recognition dataset called Celex-Gait is created.Experimental results show that VEGNN achieves a gait recognition accuracy of 95.8%and 93.9%on the Celex-Gait dataset and the DVS128-Gait-Day dataset,respectively.Furthermore,compared to the state-of-the-art methods,VEGNN reduces the number of model parameters by 30%.This indicates that VEGNN achieves higher recognition accuracy with lower model complexity. 展开更多
关键词 selects representative voxels voxel event graph neural network vegnn event stream lightweight feature extraction network voxel event graph neural network graph structurefinallya graph neural networks gnns gait recognitiona
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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Dual Channel Graph Convolutional Networks via Personalized PageRank 认领 引用
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作者 Longlong Lin Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期221-223,共3页
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat... Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications. 展开更多
关键词 convolutional node feature similarity graph convolutional framework learning graph representations neural networks gnns networks graph personalized
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CGGM:a conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks 认领 引用
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作者 Munan Li Xianshi Su +3 位作者 Runze Ma Tongbang Jiang Zijian Li Tony Q.S.Quek 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期686-697,共12页
Improving the energy efficiency of information transmission is very critical to the development of future Internet of Things(IoT).Considering the sporadic characteristics for IoT transmissions,the energy consumption o... Improving the energy efficiency of information transmission is very critical to the development of future Internet of Things(IoT).Considering the sporadic characteristics for IoT transmissions,the energy consumption of a specific transmission session significantly varies with channel condition and Quality of Service(QoS)requirements.In this study,we focus on the analysis and optimization for wireless relaying communications'statistical energy consumption.Particularly,we investigate a wirelessly-powered DF relaying communication system.Under Time Switching(TS)and Power Splitting(PS)modes,we analyze and minimize the statistical energy consumption of transmitting a fixed amount of data using mathematical analysis.Through showing some selected numerical examples,we discuss various design tradeoffs.These results will provide some important guidelines for the design of green IoT communication systems. 展开更多
关键词 Anomaly detection Graph neural network Temporal graph embedding Network traffic Graph generation
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Dynamic Knowledge Graph Reasoning Based on Distributed Representation Learning 认领 引用
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作者 Qiuru Fu Shumao Zhang +4 位作者 Shuang Zhou Jie Xu Changming Zhao Shanchao Li Du Xu 《Computers, Materials & Continua》 SCIE EI 2026年第2期1542-1560,共19页
Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowled... Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance. 展开更多
关键词 Dynamic knowledge graph reasoning recurrent neural network graph convolutional network graph attention mechanism
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Advanced High-Order Graph Convolutional Networks With Assorted Time-Frequency Transforms 认领 引用
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作者 Ling Wang Ye Yuan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期394-408,共15页
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa... A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance. 展开更多
关键词 Dynamic graph(DG)learning ensemble graph representation learning high-order graph convolution network(HGCN) time-frequency transform tensor product
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Automatic Detection of Health-Related Rumors: A Dual-Graph Collaborative Reasoning Framework Based on Causal Logic and Knowledge Graph 认领 引用
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作者 Ning Wang Haoran Lyu Yuchen Fu 《Computers, Materials & Continua》 SCIE EI 2026年第1期2163-2193,共31页
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p... With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media. 展开更多
关键词 Health rumor detection causal graph knowledge graph dual-graph fusion
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Spatio-temporal feature extraction with a global-local Transformer model for video scene graph generation 认领 引用
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作者 Rongsen Wu Jie Xu +4 位作者 Hao Zheng Zhiyuan Xu Zixuan Li Shixue Cheng Shumao Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期364-374,共11页
In the field of video scene graph generation,spatio-temporal feature extraction and the long-tail effect in relationship classification are core research issues.This paper proposes extracting spatio-temporal features ... In the field of video scene graph generation,spatio-temporal feature extraction and the long-tail effect in relationship classification are core research issues.This paper proposes extracting spatio-temporal features using the global-local Transformer model for video scene graph generation.Methods based on the Transformer architecture and attention mechanism enrich the semantic information of spatio-temporal features in videos,thereby improving the accuracy of relationship classification.In the feature processing module,pose features are introduced to strengthen the semantic representation of objects.In the spatial feature encoding module,a local spatial visibility matrix based on bounding boxes and key points of human pose features is proposed to add the issue of insufficient attention to local details in traditional Transformer encoders.In the temporal feature encoding module,a global random frame extraction strategy is proposed,which considers global temporal features while also taking computational complexity into account.In the relation classification module,to address the uneven distribution of object and relation categories in the Action Genome dataset,a relation classification loss function based on bipartite graph matching and Focal Loss is proposed,which alleviates the long-tail effect in relation classification and improves the accuracy. 展开更多
关键词 Video scene graph generation Transformer Pose features Visibility matrix Bipartite graph matching
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KIG:A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation 认领 引用
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作者 Siyuan Xu Dong Pan +3 位作者 Zhaohui Jiang Zhiwen Chen Haoyang Yu Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期327-345,共19页
Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider... Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS. 展开更多
关键词 Blast furnace ironmaking process graph neural network(GNN) knowledge graph(KG) multisensor time series(MTS) temporal alignment time-delay estimation(TDE)
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Graph Attention Networks for Skin Lesion Classification with CNN-Driven Node Features 认领 引用
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作者 Ghadah Naif Alwakid Samabia Tehsin +3 位作者 Mamoona Humayun Asad Farooq Ibrahim Alrashdi Amjad Alsirhani 《Computers, Materials & Continua》 SCIE EI 2026年第1期1964-1984,共21页
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ... Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems. 展开更多
关键词 Graph neural network image classification DermaMNIST dataset graph representation
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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 systems an... 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 κ-core and κ-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-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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Few-Shot Knowledge Graph Completion with Structure-Aware Graph Attention Network 认领 引用
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作者 YANG Rongtai SHAO Yubin +2 位作者 DU Qingzhi ZHANG Feng QI Yuting 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期1024-1033,I0019,共10页
Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity'... Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity's neighborhood topology holds potential to address this,its significance is overlooked in current research.In this paper,we propose a structure-aware graph attention network for few-shot knowledge graph completion.Firstly,to enhance entity representations,we design a structure-aware graph attention encoder to capture the graph's structural features of nodes,generating embedding for entity pairs.Secondly,a semantic prototype matching network is employed to compute the prediction score.Experiments on the NELL-One and Wiki-One datasets show that our proposed model outperforms the best baseline models by 0.021,0.026,0.039,0.032 and 0.016,0.064,0.043,0.040 in terms of MRR,Hits@10,Hits@5,and Hits@1 metrics,respectively.This demonstrates that our model can effectively leverage neighborhood topological information to improve the accuracy of knowledge completion,and achieve a better generalization. 展开更多
关键词 knowledge graph completion neighborhood topology structure-aware graph attention entity representations semantic prototype
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Spatial-Temporal Graph Fusion with Dual-Scale Convolution for Traffic Flow Prediction 认领 引用
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作者 Dan Wang Mengyi Cui +1 位作者 Zhenhua Yu Yukang Liu 《Computers, Materials & Continua》 SCIE EI 2026年第6期1375-1396,共22页
Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal f... Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal features,particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks.This paper proposes a novel framework of traffic flow prediction,referred to as Adaptive Graph Fusion Dual-scale Convolutional Network(AGFDCN),which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks.Specifically,we introduce a Dual-Scale Temporal Network,which combines long-and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns across multiple temporal scales.Furthermore,we design a Dynamic Adaptive Graph Module,which models complex spatial dependencies in traffic networks through an adaptive graph fusion mechanism and a dual-path attention-gated module.Finally,the temporal and spatial representations are integrated by employing a gated fusion mechanism,enhancing the overall prediction performance.Experimental results obtained based on three highway datasets(i.e.,PEMS04,PEMS07 and PEMS08)verify that the proposed model outperforms several state-of-the-art baselines in various evaluation metrics.Compared to the spatial-temporal graph model AGCRN with best performance in the baseline models,the proposed model exhibits significant improvements across all datasets:it achieves reduces of MAE by 42.07%and RMSE by 35.43%on PEMS04;MAE by 28.35%and RMSE by 29.28%on PEMS07;and MAE by 30.52%and RMSE by 30.73%on PEMS08,respectively,validating its effectiveness in modeling complex spatial-temporal traffic data and its robustness in handling sudden traffic changes. 展开更多
关键词 Dual-scale convolution dual-path attention-gated module adaptive graph fusion spatial-temporal dynamic graph traffic flow prediction
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The 3-decomposition conjecture of cubic graphs 认领 引用
15
作者 Genghua Fan Shanshan Guo Chuixiang Zhou 《Science China Mathematics》 SCIE CSCD 2026年第8期2239-2248,共10页
The 3-decomposition conjecture states that every connected cubic graph can be decomposed into a spanning tree,a union of cycles,and a matching.In this paper,we use a rooted spanning tree as a tool to show that every c... The 3-decomposition conjecture states that every connected cubic graph can be decomposed into a spanning tree,a union of cycles,and a matching.In this paper,we use a rooted spanning tree as a tool to show that every connected cubic graph of size n can be decomposed into a spanning tree,a union of cycles,a matching,and a union of at most (n-4)/8 paths of length 2. 展开更多
关键词 graph decomposition cubic graph 3-decomposition conjecture rooted spanning tree
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Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
16
作者 Pangwei Wang Jie Wang +2 位作者 Zipeng Wang Hangrui Dong Li Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1417-1435,共19页
Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The ... Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System(ITS).However,sensors are vulnerable to environmental interference,which can result in data loss at specific points or along arterial highways for certain periods,potentially undermining system safety and decision-making reliability.To address these challenges,a deep learning method based on Graph Convolutional Networks(GCN)and Gated Recurrent Units(GRU)is proposed,leveraging Artificial Intelligence(AI)and intelligent connected technologies for real-time acquisition of multi-sensor perception data.A feature-level fusion integrates multi-source perception data.GCN captures spatial dependencies from the road network topology,while GRU extracts temporal features from time series,enabling accurate imputation of missing traffic data.The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area.Results show that the accuracy of long-term traffic state completion reaches 89.36%,and the Root Mean Square Error(RMSE)is reduced by 17.2%compared to the Long Short-Term Memory(LSTM)baseline.This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS. 展开更多
关键词 Intelligent transportation information security traffic information completion traffic holographic perception AI-driven edge computing graph convolutional neural network
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SSAG:Situational Semantic Augmented Graph for Active SLAM in Object-Goal Navigation 认领 引用
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作者 Shasha Tian Zhengyang Chen +4 位作者 Kai Ren Na Li Chongwei Ruan Zhijia Cui Mian Wu 《Computers, Materials & Continua》 SCIE EI 2026年第9期452-473,共22页
To address the issues of low exploration efficiency and“geometric myopia”caused by the lack of highlevel environmental structure modeling for mobile robots in complex indoor environments,this paper proposes an activ... To address the issues of low exploration efficiency and“geometric myopia”caused by the lack of highlevel environmental structure modeling for mobile robots in complex indoor environments,this paper proposes an active SLAMobject navigationmethod based on Situational Semantic Augmented Graph(SSAG).Unlikemethods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association,this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions.First,an online room segmentation algorithmis employed to transformunstructured sensory data into a structured graph representation that characterizes room-level functional attributes and topological associations.Subsequently,a Graph Attention Network(GAT)is utilized to perform explicit reasoning on the semantic attributes of unobserved regions,providing decision-making priors for long-range exploration.Building upon this,we introduce a Product of Experts(PoE)mechanism within the Proximal Policy Optimization(PPO)framework to deeply fuse semantic reasoning heatmaps with geometric hard constraints.This integration optimizes the target selection strategy,generating long-term navigation goals with superior semantic consistency and physical reachability.Experimental results on the Habitat simulator and Gibson dataset demonstrate that the proposed SSAG achieves a Success weighted by Path Length(SPL)of 0.340,outperforming SemExp and SemGO by 15.3%and 4.9%,respectively,with a total success rate of 68.4%.Notably,for object search tasks with strong spatial correlations(e.g.,“bed”and“toilet”),the success rates reach 73.2%and 72.5%,representing a performance gain of over 20%compared to baseline methods.These results validate the effectiveness of room-level semantic reasoning for object navigation and demonstrate the capability of the proposed method to achieve efficient autonomous navigation in unknown,complex scenarios. 展开更多
关键词 Active SLAM object navigation situational semantic augmented graph graph attention network deep reinforcement learning
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Hypergraphs for Covering Trees and Approximating Steiner Trees 认领 引用
18
作者 Miklós Molnár 《Computers, Materials & Continua》 SCIE EI 2026年第7期2171-2185,共15页
This article presents a particular tree covering technique.To cover a set of nodes belonging to an unknown tree,a set of connected small Steiner trees is proposed.These Steiner trees can be represented as hyperedges,a... This article presents a particular tree covering technique.To cover a set of nodes belonging to an unknown tree,a set of connected small Steiner trees is proposed.These Steiner trees can be represented as hyperedges,and a chain or tree of hyperedges provides the cover.The model allows the calculation of approximate(partial)spanning trees in graphs.The idea of covering a set of nodes by hyperedges can be used directly in Steiner heuristics.The NP-hard Steiner problem in graphs is one of the most studied graph-related problems.Several heuristics are known to give approximated solutions.The classical approximations of the Steiner problem apply shortest paths.We present a generalized metric closure that can be constructed from hyperedges of limited size,and new approximations based on the generalized metric closure are proposed.A connected hyperedge set(without loops)approximates a Steiner tree.The paper also presents a performance analysis of the proposed heuristics.The variation in hyperedge size is analyzed.An interesting result is that using larger hyperedges significantly improves the algorithm’s efficiency. 展开更多
关键词 Graphs spanning problems hypergraphs steiner problem metric closure approximation
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Sharp Bounds for ABS Index of Line,Total and Mycielski Graphs 认领 引用
19
作者 YE Qingfang LI Fengwei 《数学进展》 CSCD 北大核心 2026年第1期45-59,共15页
The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isome... The atom-bond sum-connectivity(ABS)index,put forward by[J.Math.Chem.,2022,60(10):20812093],exhibits a strong link with the acentric factor of octane isomers.The experimental physico-chemical properties of octane isomers,such as boiling point,of formation are found to be better measured by the ABS index than by the Randi,atom-bond connectivity(ABC),and sum-connectivity(SC)indices.One important source of information for researching the molecular structure is the bounds for its topological indices.The extrema of the ABS index of the line,total,and Mycielski graphs are calculated in this work.Moreover,the pertinent extremal graphs were illustrated. 展开更多
关键词 ABS index line graph total graph Mycielski graph
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