The majority of existing graph-network-based few-shot models focus on a node-similarity update mode.The lack of adequate information intensies the risk of overtraining.In this paper,we propose a novel Multihead Attent...The majority of existing graph-network-based few-shot models focus on a node-similarity update mode.The lack of adequate information intensies the risk of overtraining.In this paper,we propose a novel Multihead Attention Graph Network to excavate discriminative relation and fulll effective information propagation.For edge update,the node-level attention is used to evaluate the similarities between the two nodes and the distributionlevel attention extracts more in-deep global relation.The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature.For node update,we embrace the label-level attention to soften the noise of irrelevant nodes and optimize the update direction.Our proposed model is veried through extensive experiments on two few-shot benchmark MiniImageNet and CIFAR-FS dataset.The results suggest that our method has a strong capability of noise immunity and quick convergence.The classication accuracy outperforms most state-of-the-art approaches.展开更多
In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a n...In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.展开更多
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
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode...Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.展开更多
Numerous meta-learning methods focus on the few-shot learning issue,yet most of them assume that various tasks have a shared embedding space,so the generalization ability of the trained model is limited.In order to so...Numerous meta-learning methods focus on the few-shot learning issue,yet most of them assume that various tasks have a shared embedding space,so the generalization ability of the trained model is limited.In order to solve the aforementioned problem,a task-adaptive meta-learning method based on graph neural network(TAGN) is proposed in this paper,where the characterization ability of the original feature extraction network is ameliorated and the classification accuracy is remarkably improved.Firstly,a task-adaptation module based on the self-attention mechanism is employed,where the generalization ability of the model is enhanced on the new task.Secondly,images are classified in non-Euclidean domain,where the disadvantages of poor adaptability of the traditional distance function are overcome.A large number of experiments are conducted and the results show that the proposed methodology has a better performance than traditional task-independent classification methods on two real-word datasets.展开更多
Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous ap...Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global features.However,these approaches typically neglect the topological structure information of the graph during the global feature extraction stage.This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image classification.Our approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual images.Recognizing that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global features.Additionally,we introduce a G-LFF module to fuse the local and global features effectively.展开更多
Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Ea...Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules,where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To evaluate the robustness,we add natural background noise to the model input and find that EQGraphNet achieves the best results,particularly for signals with lower signal-to-noise ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,by demonstrating the effectiveness of deeper architectures,this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.展开更多
Session-based recommendation systems(SBR)are pivotal in suggesting items by analyzing anonymized sequences of user interactions.Traditional methods,while competent,often fall short in two critical areas:they fail to a...Session-based recommendation systems(SBR)are pivotal in suggesting items by analyzing anonymized sequences of user interactions.Traditional methods,while competent,often fall short in two critical areas:they fail to address potential inter-session item transitions,which are behavioral dependencies that extend beyond individual session boundaries,and they rely on monolithic item aggregation to construct session representations.This approach does not capture the multi-scale and heterogeneous nature of user intent,leading to a decrease in modeling accuracy.To overcome these limitations,a novel approach called HMGS has been introduced.This system incorporates dual graph architectures to enhance the recommendation process.A global transition graph captures latent cross-session item dependencies,while a heterogeneous intra-session graph encodesmulti-scale item embeddings through localized feature propagation.Additionally,amulti-tier graphmatchingmechanism aligns user preference signals across different granularities,significantly improving interest localization accuracy.Empirical validation on benchmark datasets(Tmall and Diginetica)confirms HMGS’s efficacy against state-of-the-art baselines.Quantitative analysis reveals performance gains of 20.54%and 12.63%in Precision@10 on Tmall and Diginetica,respectively.Consistent improvements are observed across auxiliary metrics,with MRR@10,Precision@20,and MRR@20 exhibiting enhancements between 4.00%and 21.36%,underscoring the framework’s robustness in multi-faceted recommendation scenarios.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
Traditional Chinese medicine(TCM)features complex compatibility mechanisms involving multicomponent,multi-target,and multi-pathway interactions.This study presents an interpretable graph artificial intelligence(GraphA...Traditional Chinese medicine(TCM)features complex compatibility mechanisms involving multicomponent,multi-target,and multi-pathway interactions.This study presents an interpretable graph artificial intelligence(GraphAI)framework to quantify such mechanisms in Chinese herbal formulas(CHFs).A multidimensional TCM knowledge graph(TCM-MKG;https://zenodo.org/records/13763953)was constructed,integrating seven standardized modules:TCM terminology,Chinese patent medicines(CPMs),Chinese herbal pieces(CHPs),pharmacognostic origins(POs),chemical compounds,biological targets,and diseases.A neighbor-diffusion strategy was used to address the sparsity of compound-target associations,increasing target coverage from 12.0%to 98.7%.Graph neural networks(GNNs)with attention mechanisms were applied to 6,080 CHFs,modeled as graphs with CHPs as nodes.To embed domain-specific semantics,virtual nodes medicinal properties,i.e.,therapeutic nature,flavor,and meridian tropism,were introduced,enabling interpretable modeling of inter-CHP relationships.The model quantitatively captured classical compatibility roles such as“monarch-minister-assistant-guide”,and uncovered TCM etiological types derived from diagnostic and efficacy patterns.Model validation using 215 CHFs used for coronavirus disease 2019(COVID-19)management highlighted Radix Astragali-Rhizoma Phragmitis as a high-attention herb pair.Mass spectrometry(MS)and target prediction identified three active compounds,i.e.,methylinissolin-3-O-glucoside,corydalin,and pingbeinine,which converge on pathways such as neuroactive ligand-receptor interaction,xenobiotic response,and neuronal function,supporting their neuroimmune and detoxification potential.Given their high safety and dietary compatibility,this herb pair may offer therapeutic value for managing long COVID-19.All data and code are openly available(https://github.com/ZENGJingqi/GraphAI-for-TCM),providing a scalable and interpretable platform for TCM mechanism research and discovery of bioactive herbal constituents.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting it...Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.展开更多
With the rapid development of deep learning neural networks,new solutions have emerged for addressing fluid flow problems in porous media.Combining data-driven approaches with physical constraints has become a hot res...With the rapid development of deep learning neural networks,new solutions have emerged for addressing fluid flow problems in porous media.Combining data-driven approaches with physical constraints has become a hot research direction,with physics-informed neural networks(PINNs) being the most popular hybrid model.PINNs have gained widespread attention in subsurface fluid flow simulations due to their low computational resource requirements,fast training speeds,strong generalization capabilities,and broad applicability.Despite success in homogeneous settings,standard PINNs face challenges in accurately calculating flux between irregular Eulerian cells with disparate properties and capturing global field influences on local cells.This limits their suitability for heterogeneous reservoirs and the irregular Eulerian grids frequently used in reservoir.To address these challenges,this study proposes a physics-informed graph neural network(PIGNN) model.The PIGNN model treats the entire field as a whole,integrating information from neighboring grids and physical laws into the solution for the target grid,thereby improving the accuracy of solving partial differential equations in heterogeneous and Eulerian irregular grids.The optimized model was applied to pressure field prediction in a spatially heterogeneous reservoir,achieving an average L_(2) error and R_(2) score of 6.710×10^(-4)and 0.998,respectively,which confirms the effectiveness of model.Compared to the conventional PINN model,the average L_(2) error was reduced by 76.93%,the average R_(2) score increased by 3.56%.Moreover,evaluating robustness,training the PIGNN model using only 54% and 76% of the original data yielded average relative L_(2) error reductions of 58.63% and 56.22%,respectively,compared to the PINN model.These results confirm the superior performance of this approach compared to PINN.展开更多
Schizophrenia(SZ)stands as a severe psychiatric disorder.This study applied diffusion tensor imaging(DTI)data in conjunction with graph neural networks to distinguish SZ patients from normal controls(NCs)and showcases...Schizophrenia(SZ)stands as a severe psychiatric disorder.This study applied diffusion tensor imaging(DTI)data in conjunction with graph neural networks to distinguish SZ patients from normal controls(NCs)and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features,achieving an accuracy of 73.79%in distinguishing SZ patients from NCs.Beyond mere discrimination,our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis.These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers,providing novel insights into the neuropathological basis of SZ.In summary,our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.展开更多
Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,...Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry.展开更多
Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering n...Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials.Unlike traditional molecular graphs,crystal graphs require consideration of periodic invariance and modes.In addition,MOF structures such as covalent bonds,functional groups,and global structures impact adsorption performance in different ways.However,redundant atomic interactions can disrupt training accuracy,potentially leading to overfitting.In this paper,we propose a multi-scale crystal graph for describing periodic crystal structures,modeling interatomic interactions at different scales while preserving periodicity invariance.We also propose a multi-head attention crystal graph network in multi-scale graphs(MHACGN-MS),which learns structural characteristics by focusing on interatomic interactions at different scales,thereby reducing interference from redundant interactions.Using MOF adsorption for gases as an example,we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption.We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.展开更多
基金supported in part by the Natural Science Foundation of China under Grant 61972169 and U1536203in part by the National key research and developm program of China(2016QY01W0200)in part by the Major Scientic and Technological Project of Hubei Province(2018AAA068 and 2019AAA051).
文摘The majority of existing graph-network-based few-shot models focus on a node-similarity update mode.The lack of adequate information intensies the risk of overtraining.In this paper,we propose a novel Multihead Attention Graph Network to excavate discriminative relation and fulll effective information propagation.For edge update,the node-level attention is used to evaluate the similarities between the two nodes and the distributionlevel attention extracts more in-deep global relation.The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature.For node update,we embrace the label-level attention to soften the noise of irrelevant nodes and optimize the update direction.Our proposed model is veried through extensive experiments on two few-shot benchmark MiniImageNet and CIFAR-FS dataset.The results suggest that our method has a strong capability of noise immunity and quick convergence.The classication accuracy outperforms most state-of-the-art approaches.
基金the National Natural Science Foundation of China (Grant No. 11702289)Key core technology and generic technology research and development project of Shanxi Province of China (Grant No. 2020XXX013)the National Key Research and Development Project of China。
文摘In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.
基金support by the National Natural Science Foundation of China(NSFC)under grant number 61873274.
文摘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.
基金Youth Innovation Promotion Association CAS,Grant/Award Number:2021103Strategic Priority Research Program of Chinese Academy of Sciences,Grant/Award Number:XDC02060500。
文摘Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.
基金Supported by the National High Technology Research and Development Program of China(20-H863-05-XXX-XX)the National Natural Science Foundation of China(61171131)+1 种基金Shandong Province Key Research and Development Program(YD01033)the China Scholarship Council Program(201608370049)。
文摘Numerous meta-learning methods focus on the few-shot learning issue,yet most of them assume that various tasks have a shared embedding space,so the generalization ability of the trained model is limited.In order to solve the aforementioned problem,a task-adaptive meta-learning method based on graph neural network(TAGN) is proposed in this paper,where the characterization ability of the original feature extraction network is ameliorated and the classification accuracy is remarkably improved.Firstly,a task-adaptation module based on the self-attention mechanism is employed,where the generalization ability of the model is enhanced on the new task.Secondly,images are classified in non-Euclidean domain,where the disadvantages of poor adaptability of the traditional distance function are overcome.A large number of experiments are conducted and the results show that the proposed methodology has a better performance than traditional task-independent classification methods on two real-word datasets.
基金supported by the National Natural Science Foundation of China(62276092,62303167)Key Science and Technology Program of Henan Province(212102310084)+11 种基金MRC(MC_PC_17171)Royal Society(RP202G0230)BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(P202PF11).Sino-UK Industrial Fund(RP202G0289)LIAS(P202ED10,P202RE969)Key Scientific Research Projects of Colleges and Universities in Henan Province(25A520009)Data Science Enhancement Fund(P202RE237)Fight for Sight(24NN201)Sino-UK Education Fund(OP202006)BBSRC(RM32G0178B8).
文摘Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global features.However,these approaches typically neglect the topological structure information of the graph during the global feature extraction stage.This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image classification.Our approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual images.Recognizing that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global features.Additionally,we introduce a G-LFF module to fuse the local and global features effectively.
基金supported by the National Natural Science Foundation of China under Grant 41974137.
文摘Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules,where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To evaluate the robustness,we add natural background noise to the model input and find that EQGraphNet achieves the best results,particularly for signals with lower signal-to-noise ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,by demonstrating the effectiveness of deeper architectures,this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.
基金funded by the State Grid Hebei Electric Power Company(Project Number:KJ2023-093).
文摘Session-based recommendation systems(SBR)are pivotal in suggesting items by analyzing anonymized sequences of user interactions.Traditional methods,while competent,often fall short in two critical areas:they fail to address potential inter-session item transitions,which are behavioral dependencies that extend beyond individual session boundaries,and they rely on monolithic item aggregation to construct session representations.This approach does not capture the multi-scale and heterogeneous nature of user intent,leading to a decrease in modeling accuracy.To overcome these limitations,a novel approach called HMGS has been introduced.This system incorporates dual graph architectures to enhance the recommendation process.A global transition graph captures latent cross-session item dependencies,while a heterogeneous intra-session graph encodesmulti-scale item embeddings through localized feature propagation.Additionally,amulti-tier graphmatchingmechanism aligns user preference signals across different granularities,significantly improving interest localization accuracy.Empirical validation on benchmark datasets(Tmall and Diginetica)confirms HMGS’s efficacy against state-of-the-art baselines.Quantitative analysis reveals performance gains of 20.54%and 12.63%in Precision@10 on Tmall and Diginetica,respectively.Consistent improvements are observed across auxiliary metrics,with MRR@10,Precision@20,and MRR@20 exhibiting enhancements between 4.00%and 21.36%,underscoring the framework’s robustness in multi-faceted recommendation scenarios.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported by the National Natural Science Foundation of China(Grant No.:82230117).
文摘Traditional Chinese medicine(TCM)features complex compatibility mechanisms involving multicomponent,multi-target,and multi-pathway interactions.This study presents an interpretable graph artificial intelligence(GraphAI)framework to quantify such mechanisms in Chinese herbal formulas(CHFs).A multidimensional TCM knowledge graph(TCM-MKG;https://zenodo.org/records/13763953)was constructed,integrating seven standardized modules:TCM terminology,Chinese patent medicines(CPMs),Chinese herbal pieces(CHPs),pharmacognostic origins(POs),chemical compounds,biological targets,and diseases.A neighbor-diffusion strategy was used to address the sparsity of compound-target associations,increasing target coverage from 12.0%to 98.7%.Graph neural networks(GNNs)with attention mechanisms were applied to 6,080 CHFs,modeled as graphs with CHPs as nodes.To embed domain-specific semantics,virtual nodes medicinal properties,i.e.,therapeutic nature,flavor,and meridian tropism,were introduced,enabling interpretable modeling of inter-CHP relationships.The model quantitatively captured classical compatibility roles such as“monarch-minister-assistant-guide”,and uncovered TCM etiological types derived from diagnostic and efficacy patterns.Model validation using 215 CHFs used for coronavirus disease 2019(COVID-19)management highlighted Radix Astragali-Rhizoma Phragmitis as a high-attention herb pair.Mass spectrometry(MS)and target prediction identified three active compounds,i.e.,methylinissolin-3-O-glucoside,corydalin,and pingbeinine,which converge on pathways such as neuroactive ligand-receptor interaction,xenobiotic response,and neuronal function,supporting their neuroimmune and detoxification potential.Given their high safety and dietary compatibility,this herb pair may offer therapeutic value for managing long COVID-19.All data and code are openly available(https://github.com/ZENGJingqi/GraphAI-for-TCM),providing a scalable and interpretable platform for TCM mechanism research and discovery of bioactive herbal constituents.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金financial support from the National Natural Science Foundation of China(Grant Nos.71971079,72271087,and 71871088)the Major Projects of the National Social Science Foundation of China(Grant No.21ZDA114)+1 种基金the National Social Science Foundation of China(Grant No.19BTJ018)the Hunan Provincial Natural Science Foundation of China(Grant No.21JJ20019).
文摘Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.
基金supported by the National Natural Science Foundation of China (No. 52274048)Beijing Natural Science Foundation (No. 3222037)。
文摘With the rapid development of deep learning neural networks,new solutions have emerged for addressing fluid flow problems in porous media.Combining data-driven approaches with physical constraints has become a hot research direction,with physics-informed neural networks(PINNs) being the most popular hybrid model.PINNs have gained widespread attention in subsurface fluid flow simulations due to their low computational resource requirements,fast training speeds,strong generalization capabilities,and broad applicability.Despite success in homogeneous settings,standard PINNs face challenges in accurately calculating flux between irregular Eulerian cells with disparate properties and capturing global field influences on local cells.This limits their suitability for heterogeneous reservoirs and the irregular Eulerian grids frequently used in reservoir.To address these challenges,this study proposes a physics-informed graph neural network(PIGNN) model.The PIGNN model treats the entire field as a whole,integrating information from neighboring grids and physical laws into the solution for the target grid,thereby improving the accuracy of solving partial differential equations in heterogeneous and Eulerian irregular grids.The optimized model was applied to pressure field prediction in a spatially heterogeneous reservoir,achieving an average L_(2) error and R_(2) score of 6.710×10^(-4)and 0.998,respectively,which confirms the effectiveness of model.Compared to the conventional PINN model,the average L_(2) error was reduced by 76.93%,the average R_(2) score increased by 3.56%.Moreover,evaluating robustness,training the PIGNN model using only 54% and 76% of the original data yielded average relative L_(2) error reductions of 58.63% and 56.22%,respectively,compared to the PINN model.These results confirm the superior performance of this approach compared to PINN.
基金supported by the National Natural Science Foundation of China(62276049,61701078,61872068,and 62006038)the Natural Science Foundation of Sichuan Province(2025ZNSFSC0487)+3 种基金the Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200)the National Key R&D Program of China(2023YFE0118600)Sichuan Province Science and Technology Support Program(2019YJ0193,2021YFG0126,2021YFG0366,and 2022YFS0180)Medico-Engineering Cooperation Funds from the University of Electronic Science and Technology of China(ZYGX2021YGLH014).
文摘Schizophrenia(SZ)stands as a severe psychiatric disorder.This study applied diffusion tensor imaging(DTI)data in conjunction with graph neural networks to distinguish SZ patients from normal controls(NCs)and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features,achieving an accuracy of 73.79%in distinguishing SZ patients from NCs.Beyond mere discrimination,our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis.These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers,providing novel insights into the neuropathological basis of SZ.In summary,our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
基金supported by National Natural Science Foundation of China(Nos.41872150,U2344209 and U19B6003)the PetroChina Southwest Oil and Gasfield Company(No.2020-54365)。
文摘Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry.
基金supported by the National Natural Science Foundation of China(NSFC)(61821005).
文摘Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials.Unlike traditional molecular graphs,crystal graphs require consideration of periodic invariance and modes.In addition,MOF structures such as covalent bonds,functional groups,and global structures impact adsorption performance in different ways.However,redundant atomic interactions can disrupt training accuracy,potentially leading to overfitting.In this paper,we propose a multi-scale crystal graph for describing periodic crystal structures,modeling interatomic interactions at different scales while preserving periodicity invariance.We also propose a multi-head attention crystal graph network in multi-scale graphs(MHACGN-MS),which learns structural characteristics by focusing on interatomic interactions at different scales,thereby reducing interference from redundant interactions.Using MOF adsorption for gases as an example,we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption.We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.
