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Semi-Supervised Graph Learning for Brain Disease Identification
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作者 Kunpeng Zhang Yining Zhang Xueyan Liu 《Journal of Applied Mathematics and Physics》 2023年第7期1846-1859,共14页
Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely use... Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely used, where graph represents the similarity between patients or brain regions of interest. In these models, constructing high-quality graphs is of paramount importance. Researchers have proposed various methods for constructing graphs from different perspectives, among which the simplest and most popular one is Pearson Correlation (PC). Although existing methods have achieved significant results, these graphs are usually fixed once they are constructed, and are generally operated separately from downstream task. Such a separation may result in neither the constructed graph nor the extracted features being ideal. To solve this problem, we use the graph-optimized locality preserving projection algorithm to extract features and the population graph simultaneously, aiming in higher identification accuracy through a task-dependent automatic optimization of the graph. At the same time, we incorporate supervised information to enable more flexible modelling. Specifically, the proposed method first uses PC to construct graph as the initial feature for each subject. Then, the projection matrix and graph are iteratively optimized through graph-optimization locality preserving projections based on semi-supervised learning, which fully employs the knowledge in various transformation spaces. Finally, the obtained projection matrix is applied to construct the subject-level graph and perform classification using support vector machines. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs), and the results showed that the classification performance of our method is better than that of the baseline method. 展开更多
关键词 graph learning Mild Cognitive Impairment Autism Spectrum Disorder
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A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment
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作者 Weijian Song Xi Li +3 位作者 Peng Chen Juan Chen Jianhua Ren Yunni Xia 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期3001-3016,共16页
With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasin... With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly prominent.Thus,it is crucial to detect anomalies in the collected IoT time series from various sensors.Recently,deep learning models have been leveraged for IoT anomaly detection.However,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning techniques.Nevertheless,the absence of accurate abnormal information in unsupervised learning methods limits their performance.To address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly identification.It performs better than unsupervised methods using only a small amount of labeled data.Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model.However,the dependencies between data are often unknown in time series data.To solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series data.It not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key data.Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate. 展开更多
关键词 IoT multivariate time series anomaly detection graph learning SEMI-SUPERVISED mean teachers
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Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs
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作者 Bohan Chen Kevin Miller +1 位作者 Andrea L.Bertozzi Jon Schwenk 《Communications on Applied Mathematics and Computation》 EI 2024年第2期1013-1033,共21页
Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi... Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels. 展开更多
关键词 Image segmentation graph learning Batch active learning Hyperspectral image
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Position-Aware and Subgraph Enhanced Dynamic Graph Contrastive Learning on Discrete-Time Dynamic Graph
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作者 Jian Feng Tian Liu Cailing Du 《Computers, Materials & Continua》 SCIE EI 2024年第11期2895-2909,共15页
Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information ... Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology learning.At the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network effectively.To address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs.Firstly,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the nodes.Secondly,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic graphs.Specifically,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention mechanism.Finally,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and position.Experimental results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting. 展开更多
关键词 Dynamic graph representation learning graph contrastive learning structure representation position representation evolving pattern
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Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods 被引量:2
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作者 Jiaxiang Hu Weihao Hu +5 位作者 Jianjun Chen Di Cao Zhengyuan Zhang Zhou Liu Zhe Chen Frede Blaabjerg 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2023年第1期35-51,共17页
Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures... Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures and data information of power networks.To this end,this study proposes a fault diagnostic model for distribution systems based on deep graph learning.This model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application conditions.In addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability.This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults.In addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the model.The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model.Finally,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model.Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods. 展开更多
关键词 Fault diagnosis fault location fault type analysis distribution system deep graph learning multi-task learning
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PGSLM:Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in Internet of Vehicles
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作者 Xiaozhu Liu Jiaru Zeng +1 位作者 Rongbo Zhu Hao Liu 《China Communications》 SCIE CSCD 2023年第4期270-286,共17页
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
关键词 edge computing traffic forecasting graph convolutional network graph structure learning Internet of Vehicles
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CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information
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作者 Beibei Han Yingmei Wei +1 位作者 Qingyong Wang Shanshan Wan 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1464-1479,共16页
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t... Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines. 展开更多
关键词 attributed multiplex graph network contrastive self‐supervised learning graph representation learning multiscale information
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Adaptive multi-channel Bayesian graph attention network for IoT transaction security
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作者 Zhaowei Liu Dong Yang +1 位作者 Shenqiang Wang Hang Su 《Digital Communications and Networks》 SCIE CSCD 2024年第3期631-644,共14页
With the rapid advancement of 5G technology,the Internet of Things(IoT)has entered a new phase of appli-cations and is rapidly becoming a significant force in promoting economic development.Due to the vast amounts of ... With the rapid advancement of 5G technology,the Internet of Things(IoT)has entered a new phase of appli-cations and is rapidly becoming a significant force in promoting economic development.Due to the vast amounts of data created by numerous 5G IoT devices,the Ethereum platform has become a tool for the storage and sharing of IoT device data,thanks to its open and tamper-resistant characteristics.So,Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's lives.By modeling Ethereum trans-action records as a transaction network,the account types are well identified by the Ethereum account classifi-cation system established based on Graph Neural Networks(GNNs).This work first investigates the Ethereum transaction network.Surprisingly,experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per account.This flaw may significantly impede the classification capability of GNNs,which is mostly governed by their attributes.This work proposes an Adaptive Multi-channel Bayesian Graph Attention Network(AMBGAT)for Ethereum account clas-sification to address this difficulty.AMBGAT uses attention to enhance node features,estimate graph topology that conforms to the ground truth,and efficiently extract node features pertinent to downstream tasks.An extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology. 展开更多
关键词 Internet of things graph representation learning Node classification Security mechanism
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Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism
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作者 Lanze Zhang Yijun Gu Jingjie Peng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1701-1731,共31页
Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggre... Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and embedding.However,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph topology.This characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues.Specifically,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling capabilities.For the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily assumption.We deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of them.Furthermore,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks. 展开更多
关键词 Heterophilic graph graph neural network graph representation learning failure of the homophily assumption
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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems
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作者 Sang-min Lee Namgi Kim 《Computers, Materials & Continua》 SCIE EI 2024年第2期1897-1914,共18页
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ... Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets. 展开更多
关键词 Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems
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Review on graph learning for dimensionality reduction of hyperspectral image 被引量:7
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作者 Liangpei Zhang Fulin Luo 《Geo-Spatial Information Science》 SCIE CSCD 2020年第1期98-106,共9页
Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learn... Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data. 展开更多
关键词 Hyperspectral image dimensionality reduction CLASSIFICATION graph learning
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Graph Laplacian Matrix Learning from Smooth Time-Vertex Signal 被引量:1
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作者 Ran Li Junyi Wang +2 位作者 Wenjun Xu Jiming Lin Hongbing Qiu 《China Communications》 SCIE CSCD 2021年第3期187-204,共18页
In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesia... In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesian product graph of the time-and vertex-graphs.By assuming the signals follow a Gaussian prior distribution on the joint graph,a meaningful representation that promotes the smoothness property of the joint graph signal is derived.Furthermore,by decoupling the joint graph,the graph learning framework is formulated as a joint optimization problem which includes signal denoising,timeand vertex-graphs learning together.Specifically,two algorithms are proposed to solve the optimization problem,where the discrete second-order difference operator with reversed sign(DSODO)in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm,and the time-graph,as well as the vertex-graph,is estimated by the other algorithm.Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time-and vertex-graphs from noisy and incomplete data. 展开更多
关键词 Cartesian product graph discrete secondorder difference operator Gaussian prior distribution graph Laplacian matrix learning spatiotemporal smoothness time-vertex signal
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Future Event Prediction Based on Temporal Knowledge Graph Embedding 被引量:2
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作者 Zhipeng Li Shanshan Feng +6 位作者 Jun Shi Yang Zhou Yong Liao Yangzhao Yang Yangyang Li Nenghai Yu Xun Shao 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2411-2423,共13页
Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling com... Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen. 展开更多
关键词 Event prediction temporal knowledge graph graph representation learning knowledge embedding
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A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning 被引量:1
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作者 Chuan Yang Yue Yin +2 位作者 Jiantong Zhang Penghui Ding Jian Liu 《Geoscience Frontiers》 SCIE CAS CSCD 2024年第1期29-38,共10页
The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacem... The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System(GNSS)positioning.First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes.Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement,rainfall,groundwater table and soil moisture content and the graph structure.Last introduce the state-of-the-art graph deep learning GTS(Graph for Time Series)model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system.This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system.The proposed method performs better than SVM,XGBoost,LSTM and DCRNN models in terms of RMSE(1.35 mm),MAE(1.14 mm)and MAPE(0.25)evaluation metrics,which is provided to be effective in future landslide failure early warning. 展开更多
关键词 Landslide displacement prediction GNSS positioning graph deep learning
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False Negative Sample Detection for Graph Contrastive Learning
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作者 Binbin Zhang Li Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第2期529-542,共14页
Recently,self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning,which aims to learn discriminative features for each node without label information. The key to ... Recently,self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning,which aims to learn discriminative features for each node without label information. The key to graph contrastive learning is data augmentation. The anchor node regards its augmented samples as positive samples,and the rest of the samples are regarded as negative samples,some of which may be positive samples. We call these mislabeled samples as “false negative” samples,which will seriously affect the final learning effect. Since such semantically similar samples are ubiquitous in the graph,the problem of false negative samples is very significant. To address this issue,the paper proposes a novel model,False negative sample Detection for Graph Contrastive Learning (FD4GCL),which uses attribute and structure-aware to detect false negative samples. Experimental results on seven datasets show that FD4GCL outperforms the state-of-the-art baselines and even exceeds several supervised methods. 展开更多
关键词 graph representation learning contrastive learning false negative sample detection
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Visual evaluation of graph representation learning based on the presentation of community structures
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作者 Yong Zhang Lihong Cai +5 位作者 Yuhua Liu Yize Li Songyue Li Yuming Ma Yuwei Meng Zhiguang Zhou 《Visual Informatics》 EI 2024年第3期29-31,共3页
Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep learning.However,choosing the right method for different tasks can be chall... Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep learning.However,choosing the right method for different tasks can be challenging.Communities within networks help reveal underlying structures and correlations.Investigating how different models preserve community properties is crucial for identifying the best graph representation for data analysis.This paper defines indicators to explore the perceptual quality of community properties in representation learning spaces,including the consistency of community structure,node distribution within and between communities,and central node distribution.A visualization system presents these indicators,allowing users to evaluate models based on community structures.Case studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models. 展开更多
关键词 graph representation learning Community structure Visual perception
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Graph representation learning-based residential electricity behavior identification and energy management 被引量:1
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作者 Xinpei Chen Tao Yu +2 位作者 Zhenning Pan Zihao Wang Shengchun Yang 《Protection and Control of Modern Power Systems》 SCIE EI 2023年第2期218-230,共13页
It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identificatio... It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model. 展开更多
关键词 Behavior correlation graph graph reinforcement learning Home energy management system Multi-label classification Non-intrusive load monitoring
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RSscore:Reaction superiority learned from reaction mapping hypergraph
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作者 Chenyang Xu Lijuan Guo +4 位作者 Kang Zhou Hai Yu Chaoliang Wei Fengqi Fan Lei Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2024年第10期203-215,共13页
The selection of chemical reactions is directly related to the quality of synthesis pathways,so a reasonable reaction evaluation metric plays a crucial role in the design and planning of synthesis pathways.Since react... The selection of chemical reactions is directly related to the quality of synthesis pathways,so a reasonable reaction evaluation metric plays a crucial role in the design and planning of synthesis pathways.Since reaction conditions also need to be considered in synthesis pathway design,a reaction metric that combines reaction time,temperature,and yield is required for chemical reactions of different reaction agents.In this study,a chemical reaction graph descriptor which includes the atom-atom mapping relationship is proposed to effectively describe reactions.Then,through pre-training using graph contrastive learning and fine-tuning through supervised learning,we establish a model for generating the probability of reaction superiority(RSscore).Finally,to validate the effectiveness of the current evaluation index,RSscore is applied in two applications,namely reaction evaluation and synthesis routes analysis,which proves that the RSscore provides an important agents-considered evaluation criterion for computer-aided synthesis planning(CASP). 展开更多
关键词 Computer-aided synthesis planning Neural networks Reaction evaluation indicator Reaction graph graph contractive learning
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NGAT:attention in breadth and depth exploration for semi-supervised graph representation learning
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作者 Jianke HU Yin ZHANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2022年第3期409-421,共13页
Recently,graph neural networks(GNNs)have achieved remarkable performance in representation learning on graph-structured data.However,as the number of network layers increases,GNNs based on the neighborhood aggregation... Recently,graph neural networks(GNNs)have achieved remarkable performance in representation learning on graph-structured data.However,as the number of network layers increases,GNNs based on the neighborhood aggregation strategy deteriorate due to the problem of oversmoothing,which is the major bottleneck for applying GNNs to real-world graphs.Many efforts have been made to improve the process of feature information aggregation from directly connected nodes,i.e.,breadth exploration.However,these models perform the best only in the case of three or fewer layers,and the performance drops rapidly for deep layers.To alleviate oversmoothing,we propose a nested graph attention network(NGAT),which can work in a semi-supervised manner.In addition to breadth exploration,a k-layer NGAT uses a layer-wise aggregation strategy guided by the attention mechanism to selectively leverage feature information from the k;-order neighborhood,i.e.,depth exploration.Even with a 10-layer or deeper architecture,NGAT can balance the need for preserving the locality(including root node features and the local structure)and aggregating the information from a large neighborhood.In a number of experiments on standard node classification tasks,NGAT outperforms other novel models and achieves state-of-the-art performance. 展开更多
关键词 graph learning Semi-supervised learning Node classification ATTENTION
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Interaction behavior recognition from multiple views 被引量:2
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作者 XIA Li-min GUO Wei-ting WANG Hao 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第1期101-113,共13页
This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature repr... This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition. 展开更多
关键词 local self-similarity descriptors graph shared multi-task learning composite interactive feature temporal-pyramid bag of words
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