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
Structural variations(SVs≥50 bp)are a critical but underexplored source of genetic diversity in cattle,shaping traits vital for productivity,adaptability,and health.Advances in long-read sequencing,pangenome graph co...Structural variations(SVs≥50 bp)are a critical but underexplored source of genetic diversity in cattle,shaping traits vital for productivity,adaptability,and health.Advances in long-read sequencing,pangenome graph construction,and near-complete genome assemblies now allow accurate SV detection and genotyping.These innovations overcome the limitations of single-reference genomes,enabling the discovery of complex SVs,including nested and overlapping variants,and providing access to previously inaccessible genomic regions such as centromeres and telomeres.This review highlights the current landscape of cattle SV research,with emphasis on integrating longread sequencing and pangenome frameworks to uncover breed-specific and population-level variation.While many SVs are linked to economically important traits such as feed efficiency and disease resistance,their broader regulatory impacts remain an active area of investigation.Emerging functional genomics approaches,including transcriptomics,epigenomics,and genome editing,will clarify how SVs influence gene regulation and phenotype.Looking forward,the integration of SV catalogs with multi-omics data,imputation resources,and artificial intelligence-driven models will be essential for translating discoveries into breeding and conservation applications.Integrating structural variants into breeding pipelines promises to revolutionize livestock genomics,enabling precision selection and sustainable agriculture despite challenges in cost,data sharing,and functional validation.展开更多
Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate ...Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.展开更多
This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foun...This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foundations of knowledge graph technology,the“Same Course with Different Structures”teaching model,and curriculum resource construction,and integrating existing literature,the paper analyzes the methods for constructing curriculum resources using knowledge graphs.The research finds that knowledge graphs can effectively integrate multi-source data,support personalized teaching and precision education,and provide both a scientific foundation and technical support for the development of curriculum resources within the“Same Course with Different Structures”framework.展开更多
Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can signi...Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result.展开更多
Enterprise applications utilize relational databases and structured business processes, requiring slow and expensive conversion of inputs and outputs, from business documents such as invoices, purchase orders, and rec...Enterprise applications utilize relational databases and structured business processes, requiring slow and expensive conversion of inputs and outputs, from business documents such as invoices, purchase orders, and receipts, into known templates and schemas before processing. We propose a new LLM Agent-based intelligent data extraction, transformation, and load (IntelligentETL) pipeline that not only ingests PDFs and detects inputs within it but also addresses the extraction of structured and unstructured data by developing tools that most efficiently and securely deal with respective data types. We study the efficiency of our proposed pipeline and compare it with enterprise solutions that also utilize LLMs. We establish the supremacy in timely and accurate data extraction and transformation capabilities of our approach for analyzing the data from varied sources based on nested and/or interlinked input constraints.展开更多
Structural robustness is the concept to evaluate whether local damages to the structure will cause disproportional consequences. It is one of the most important indexes to keep the structural safety, especially to con...Structural robustness is the concept to evaluate whether local damages to the structure will cause disproportional consequences. It is one of the most important indexes to keep the structural safety, especially to consider a special loading named as "human active damage". In the present paper, the loaded structure is analyzed by a weighted graph. The joints and members of the structure correspond to the vertexes and edges of the graph, and the ratio of the most dangerous stress state to the material strength of each member is treated as the weight of each edge. Based on the quantitative description of the structural topology, the structure graph is expressed as a hierarchical model which is built by a set of vertex-connected units. The local damage can be expressed as the deterioration of the unit(s), while the final possible failure mode of the structure can be obtained by a specific assignment of its weighted graph. In this way, the relationship between the structural behavior and the combined damages of the subordinate units in each hierarchy can be formed as an envelope diagram. This diagram exactly shows the contribution of each subordinate unit to the robustness of the whole structure. Furthermore, the most vulnerable part, as well as the topologic difference between the subordinates, can be found visually.展开更多
Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combin...Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combined algorithm according to the three rules for finding the essential graph of a given directed acyclic graph. Moreover, the complexity and advantages of this combined algorithm over others are also discussed. The aim of this paper is to present the proof of the correctness of the combined algorithm.展开更多
With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simu...With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously.To enhance the forecasting performance,a novel edge-enabled probabilistic graph structure learning model(PGSLM)is proposed,which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network.To obtain the spatio-temporal dependencies of traffic data,the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module.During the training process,a new graph training loss is introduced,which is composed of the K nearest neighbor(KNN)graph constructed by the traffic feature tensors and the graph structure.Detailed experimental results show that,compared with existing models,the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV.展开更多
In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower s...In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.展开更多
RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to de...RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to develop computational methods to predict RNA 3D structures.For these methods,designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges.In this study,we designed and trained a deep learning model to tackle this problem.The model was based on a graph convolutional network(GCN)and named RNAGCN.The model provided a natural way of representing RNA structures,avoided complex algorithms to preserve atomic rotational equivalence,and was capable of extracting features automatically out of structural patterns.Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions.Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions.RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.展开更多
A graph theory model of the human nature structure( GMH) for machine vision and image/graphics processing is described in this paper. Independent from the motion and deformation of contours,the human nature structure(...A graph theory model of the human nature structure( GMH) for machine vision and image/graphics processing is described in this paper. Independent from the motion and deformation of contours,the human nature structure( HNS) embodies the most basic movement characteristics of the body. The human body can be divided into basic units like head,torso,and limbs. Using these basic units,a graph theory model for the HNS can be constructed. GMH provides a basic model for human posture processing,and the outline in the perspective projection plane is the body contour of an image. In addition,the GMH can be applied to articulated motion and deformable objects,e. g.,in the design and analysis of body posture,by modifying mapping parameters of the GMH.展开更多
The paper presents the prerequisites of involving of topological elements and graph theory as an instrument of mathematical formalization of woven structures and technology of textile fabrics. Present research is base...The paper presents the prerequisites of involving of topological elements and graph theory as an instrument of mathematical formalization of woven structures and technology of textile fabrics. Present research is based on analysis and comparison of the main concepts and conditions of textile technology and graph theory.展开更多
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,...Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.展开更多
It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semanti...It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.展开更多
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 https://github.com/Tianxiang Zhao0474/WGCN.展开更多
Equipment has dual nature: physical objects existing in nature, and artificial objects designed by human. The decision on the configuration and structural parameters of equipment is made by engineers based on technica...Equipment has dual nature: physical objects existing in nature, and artificial objects designed by human. The decision on the configuration and structural parameters of equipment is made by engineers based on technical-physical effects which control the behavioral parameters of the equipment. Sensors are mounted on the equipment to monitor the equipment state. Current methods for state monitoring and diagnosis mostly use mathematics and artificial intelligence technology to construct evaluation methods. This paper presents an integrated design and state maintenance method, in which graph and dual graph are used for recording design data and sensor arrangement and for mapping method from signals to substructures and connection pairs. An example of state maintenance of hydro power generating equipment is illustrated.展开更多
Much data such as geometric image data and drawings have graph structures. Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph s...Much data such as geometric image data and drawings have graph structures. Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph structures of such data. The purpose of this paper is to find knowledge representations which indicate plural abstractions of graph structured data. Firstly, we introduce a term graph as a graph pattern having structural variables, and a substitution over term graphs which is graph rewriting system. Next, for a graph G, we define a multiple layer ( g,(θ 1,…,θ k )) of G as a pair of a term graph g and a list of k substitutions θ 1,…,θ k such that G can be obtained from g by applying substitutions θ 1,…,θ k to g. In the same way, for a set S of graphs, we also define a multiple layer for S as a pair ( D,Θ ) of a set D of term graphs and a list Θ of substitutions. Secondly, for a graph G and a set S of graphs, we present effective algorithms for extracting minimal multiple layers of G and S which give us stratifying abstractions of G and S, respectively. Finally, we report experimental results obtained by applying our algorithms to both artificial data and drawings of power plants which are real world data.展开更多
Many attentions for structural synthesis are paid to planar linkages and parallel mechanisms, while design novel pyramid deployable truss structure（PDTS） of satellite SAR mainly depends on experience of designer. To...Many attentions for structural synthesis are paid to planar linkages and parallel mechanisms, while design novel pyramid deployable truss structure（PDTS） of satellite SAR mainly depends on experience of designer. To design novel configuration of PDTS, a two-step topology structure synthesis and analysis approach is proposed. Firstly, a conceptual configuration of PDTS is synthesized. Weighted graph and weighted adjacency matrix are established to realize topological description for PDTS. Graph properties are then summarized to distinguish differentia between PDTS and other type structures. According to graph properties, a procedure for synthesis conceptual configuration of PDTS is presented. Secondly, join relationship of components in a PDTS is analyzed. Kinematic chain and corresponding incidence/adjacency matrix are employed to analyze join relationship of PDTS. Properties and simplified rules of kinematic chain are extracted to construct kinematic chain. A procedure for construction kinematic chain of PDTS is then established. Finally, with this two-step approach all 11 rectangular pyramid deployable structures whose folded state is planar are discovered and their kinematic chains are constructed. Based on synthesis results, a novel deployable support structure for satellite SAR is designed. The proposed research can be applied to obtain some novel PDTSs, which is of great importance to design some novel deployable support structures for satellite SAR antenna.展开更多
Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based s...Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.展开更多
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘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.
基金supported in part by AFRI grant numbers 2019-7015-29321 and 2021-67015-33409 from the USDA National Institute of Food and Agriculture(NIFA)the SCINet project of the USDA ARS project number 0500-00093-001-00-D。
文摘Structural variations(SVs≥50 bp)are a critical but underexplored source of genetic diversity in cattle,shaping traits vital for productivity,adaptability,and health.Advances in long-read sequencing,pangenome graph construction,and near-complete genome assemblies now allow accurate SV detection and genotyping.These innovations overcome the limitations of single-reference genomes,enabling the discovery of complex SVs,including nested and overlapping variants,and providing access to previously inaccessible genomic regions such as centromeres and telomeres.This review highlights the current landscape of cattle SV research,with emphasis on integrating longread sequencing and pangenome frameworks to uncover breed-specific and population-level variation.While many SVs are linked to economically important traits such as feed efficiency and disease resistance,their broader regulatory impacts remain an active area of investigation.Emerging functional genomics approaches,including transcriptomics,epigenomics,and genome editing,will clarify how SVs influence gene regulation and phenotype.Looking forward,the integration of SV catalogs with multi-omics data,imputation resources,and artificial intelligence-driven models will be essential for translating discoveries into breeding and conservation applications.Integrating structural variants into breeding pipelines promises to revolutionize livestock genomics,enabling precision selection and sustainable agriculture despite challenges in cost,data sharing,and functional validation.
基金supported by the Natural Science Foundation of China(No.U22A2099)the Innovation Project of Guangxi Graduate Education(YCBZ2023130).
文摘Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.
基金Educational and Teaching Reform Project of Beihua University:Research on the Construction of“Same Course with Different Structures”Course Resources Based on Knowledge Graphs。
文摘This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foundations of knowledge graph technology,the“Same Course with Different Structures”teaching model,and curriculum resource construction,and integrating existing literature,the paper analyzes the methods for constructing curriculum resources using knowledge graphs.The research finds that knowledge graphs can effectively integrate multi-source data,support personalized teaching and precision education,and provide both a scientific foundation and technical support for the development of curriculum resources within the“Same Course with Different Structures”framework.
文摘Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result.
文摘Enterprise applications utilize relational databases and structured business processes, requiring slow and expensive conversion of inputs and outputs, from business documents such as invoices, purchase orders, and receipts, into known templates and schemas before processing. We propose a new LLM Agent-based intelligent data extraction, transformation, and load (IntelligentETL) pipeline that not only ingests PDFs and detects inputs within it but also addresses the extraction of structured and unstructured data by developing tools that most efficiently and securely deal with respective data types. We study the efficiency of our proposed pipeline and compare it with enterprise solutions that also utilize LLMs. We establish the supremacy in timely and accurate data extraction and transformation capabilities of our approach for analyzing the data from varied sources based on nested and/or interlinked input constraints.
文摘Structural robustness is the concept to evaluate whether local damages to the structure will cause disproportional consequences. It is one of the most important indexes to keep the structural safety, especially to consider a special loading named as "human active damage". In the present paper, the loaded structure is analyzed by a weighted graph. The joints and members of the structure correspond to the vertexes and edges of the graph, and the ratio of the most dangerous stress state to the material strength of each member is treated as the weight of each edge. Based on the quantitative description of the structural topology, the structure graph is expressed as a hierarchical model which is built by a set of vertex-connected units. The local damage can be expressed as the deterioration of the unit(s), while the final possible failure mode of the structure can be obtained by a specific assignment of its weighted graph. In this way, the relationship between the structural behavior and the combined damages of the subordinate units in each hierarchy can be formed as an envelope diagram. This diagram exactly shows the contribution of each subordinate unit to the robustness of the whole structure. Furthermore, the most vulnerable part, as well as the topologic difference between the subordinates, can be found visually.
基金Supported by the National Natural Science Foundation of China (No. 60974082)
文摘Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combined algorithm according to the three rules for finding the essential graph of a given directed acyclic graph. Moreover, the complexity and advantages of this combined algorithm over others are also discussed. The aim of this paper is to present the proof of the correctness of the combined algorithm.
基金supported by the project of the National Natural Science Foundation of China(No.61772562)the Knowledge Innovation Program of Wuhan-Basic Research(No.2022010801010225)the Fundamental Research Funds for the Central Universities(No.2662022YJ012)。
文摘With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously.To enhance the forecasting performance,a novel edge-enabled probabilistic graph structure learning model(PGSLM)is proposed,which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network.To obtain the spatio-temporal dependencies of traffic data,the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module.During the training process,a new graph training loss is introduced,which is composed of the K nearest neighbor(KNN)graph constructed by the traffic feature tensors and the graph structure.Detailed experimental results show that,compared with existing models,the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV.
基金the Science and Technology Project of China Southern Power Grid Company,Ltd.(031200KK52200003)the National Natural Science Foundation of China(Nos.62371253,52278119).
文摘In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.
基金funded by the National Natural Science Foundation of China(Grant Nos.11774158 to JZ,11934008 to WW,and 11974173 to WFL)。
文摘RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to develop computational methods to predict RNA 3D structures.For these methods,designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges.In this study,we designed and trained a deep learning model to tackle this problem.The model was based on a graph convolutional network(GCN)and named RNAGCN.The model provided a natural way of representing RNA structures,avoided complex algorithms to preserve atomic rotational equivalence,and was capable of extracting features automatically out of structural patterns.Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions.Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions.RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.
基金Supported by the National Natural Science Foundation of China(No.71373023,61372148,61571045)Beijing Advanced Innovation Center for Imaging Technology(No.BAICIT-2016002)+1 种基金the National Key Technology R&D Program(No.2014BAK08B02,2015BAH55F03)the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(No.CIT&TCD201504039)
文摘A graph theory model of the human nature structure( GMH) for machine vision and image/graphics processing is described in this paper. Independent from the motion and deformation of contours,the human nature structure( HNS) embodies the most basic movement characteristics of the body. The human body can be divided into basic units like head,torso,and limbs. Using these basic units,a graph theory model for the HNS can be constructed. GMH provides a basic model for human posture processing,and the outline in the perspective projection plane is the body contour of an image. In addition,the GMH can be applied to articulated motion and deformable objects,e. g.,in the design and analysis of body posture,by modifying mapping parameters of the GMH.
文摘The paper presents the prerequisites of involving of topological elements and graph theory as an instrument of mathematical formalization of woven structures and technology of textile fabrics. Present research is based on analysis and comparison of the main concepts and conditions of textile technology and graph theory.
文摘Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.
基金Supported by the National Natural Science Foundation of China(61202193,61202304)the Major Projects of Chinese National Social Science Foundation(11&ZD189)+2 种基金the Chinese Postdoctoral Science Foundation(2013M540593,2014T70722)the Accomplishments of Listed Subjects in Hubei Prime Subject Developmentthe Open Foundation of Shandong Key Lab of Language Resource Development and Application
文摘It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.
基金supported by the National Natural Science Foundation of China(62225303,62403043,62433004)the Beijing Natural Science Foundation(4244085)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20230203)the China Postdoctoral Science Foundation(2023M740201)。
文摘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 https://github.com/Tianxiang Zhao0474/WGCN.
基金the National Natural Science Foundation of China(No.51175284)
文摘Equipment has dual nature: physical objects existing in nature, and artificial objects designed by human. The decision on the configuration and structural parameters of equipment is made by engineers based on technical-physical effects which control the behavioral parameters of the equipment. Sensors are mounted on the equipment to monitor the equipment state. Current methods for state monitoring and diagnosis mostly use mathematics and artificial intelligence technology to construct evaluation methods. This paper presents an integrated design and state maintenance method, in which graph and dual graph are used for recording design data and sensor arrangement and for mapping method from signals to substructures and connection pairs. An example of state maintenance of hydro power generating equipment is illustrated.
文摘Much data such as geometric image data and drawings have graph structures. Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph structures of such data. The purpose of this paper is to find knowledge representations which indicate plural abstractions of graph structured data. Firstly, we introduce a term graph as a graph pattern having structural variables, and a substitution over term graphs which is graph rewriting system. Next, for a graph G, we define a multiple layer ( g,(θ 1,…,θ k )) of G as a pair of a term graph g and a list of k substitutions θ 1,…,θ k such that G can be obtained from g by applying substitutions θ 1,…,θ k to g. In the same way, for a set S of graphs, we also define a multiple layer for S as a pair ( D,Θ ) of a set D of term graphs and a list Θ of substitutions. Secondly, for a graph G and a set S of graphs, we present effective algorithms for extracting minimal multiple layers of G and S which give us stratifying abstractions of G and S, respectively. Finally, we report experimental results obtained by applying our algorithms to both artificial data and drawings of power plants which are real world data.
基金Supported by the College Discipline Innovation Wisdom Plan in China(Grant No.B07018)National Natural Science Foundation of China(Grant Nos.50935002,11002039)
文摘Many attentions for structural synthesis are paid to planar linkages and parallel mechanisms, while design novel pyramid deployable truss structure（PDTS） of satellite SAR mainly depends on experience of designer. To design novel configuration of PDTS, a two-step topology structure synthesis and analysis approach is proposed. Firstly, a conceptual configuration of PDTS is synthesized. Weighted graph and weighted adjacency matrix are established to realize topological description for PDTS. Graph properties are then summarized to distinguish differentia between PDTS and other type structures. According to graph properties, a procedure for synthesis conceptual configuration of PDTS is presented. Secondly, join relationship of components in a PDTS is analyzed. Kinematic chain and corresponding incidence/adjacency matrix are employed to analyze join relationship of PDTS. Properties and simplified rules of kinematic chain are extracted to construct kinematic chain. A procedure for construction kinematic chain of PDTS is then established. Finally, with this two-step approach all 11 rectangular pyramid deployable structures whose folded state is planar are discovered and their kinematic chains are constructed. Based on synthesis results, a novel deployable support structure for satellite SAR is designed. The proposed research can be applied to obtain some novel PDTSs, which is of great importance to design some novel deployable support structures for satellite SAR antenna.
基金supported by the National Natural Science Fundation of China(61573285)the Doctoral Fundation of China(2013ZC53037)
文摘Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.
