Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
The adjoint method is widely used in gradient-based optimization with high-dimensional design variables.However,the cost of solving the adjoint equations in each iteration is comparable to that of solving the flow fie...The adjoint method is widely used in gradient-based optimization with high-dimensional design variables.However,the cost of solving the adjoint equations in each iteration is comparable to that of solving the flow field,resulting in expensive computational costs.To improve the efficiency of solving adjoint equations,we propose a physics-constrained graph neural networks for solving adjoint equations,named ADJ-PCGN.ADJ-PCGN establishes a mapping relationship between flow characteristics and adjoint vector based on data,serving as a replacement for the computationally expensive numerical solution of adjoint equations.A physics-based graph structure and message-passing mechanism are designed to endow its strong fitting and generalization capabilities.Taking transonic drag reduction and maximum lift-drag ratio of the airfoil as examples,results indicate that ADJ-PCGN attains a similar optimal shape as the classical direct adjoint loop method.In addition,ADJ-PCGN demonstrates strong generalization capabilities across different mesh topologies,mesh densities,and out-of-distribution conditions.It holds the potential to become a universal model for aerodynamic shape optimization involving states,geometries,and meshes.展开更多
Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequ...Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.展开更多
This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def...This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.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
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
Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective meas...Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective measures,and designing civil defense engineering solutions.Current state-of-the-art methods face several issues:Experimental research is difficult and costly to implement,theoretical research is limited to simple geometries and lacks precision,and direct simulations require substantial computational resources.To address these challenges,this paper presents a data-driven method for predicting blast loads on building surfaces.This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant,significantly improving its applicability in complex scenarios.This study introduces an innovative encoder-decoder graph neural network model named BlastGraphNet,which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events.The model also facilitates related downstream applications,such as damage mode identification and rapid assessment of virtual city explosions.The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%,and its inference speed is 3-4 orders of magnitude faster than that of state-of-the-art numerical methods.In extreme test cases involving buildings with complex geometries and building clusters,the method achieved high accuracy and excellent generalizability.The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.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
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
Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and so...Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.展开更多
Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),t...Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.展开更多
Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the d...Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.However,physiological recordings often exhibit irregular and unordered patterns,posing a significant challenge in conceptualising them as matrices.As a result,GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system.Additionally,our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain.Subsequently,demographic data are included,and a multi-task learning architecture is devised,integrating classification and regression tasks.The trials used an authentic dataset,including 800 brain x-ray pictures,consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases.Based on empirical evidence,our methodology demonstrates superior performance in classification,surpassing other comparison methods with a notable achievement of 92.27%in terms of area under the curve as well as a correlation coefficient of 0.62.展开更多
Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road ...Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road occupancy and vehicle density.Therefore,the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment.Conventional prediction systems face difficulties in identifying highly congested areas,which leads to reduced prediction accuracy.The problem is addressed by integrating Graph Neural Networks(GNN)with the Lion Swarm Optimization(LSO)framework to tackle the congestion prediction problem.Initially,the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality.Then,the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment.From the connectivity and node relationship graph,modeling improves the overall prediction accuracy.During the analysis,the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies,which helps predict high congestion on roads with minimal deviation errors.There are three core optimization phases:roaming,hunting,and migration,which enable the framework to make dynamic adjustments to enhance the predictions.The framework’s efficacy is evaluated using benchmark datasets,where the proposed work achieves 99.2%accuracy and minimizes the prediction deviation value by up to 2.5%compared to other methods.With the new framework,there was a more accurate prediction of realtime congestion,lower computational cost,and improved regulation of traffic flow.This system is easily implemented in intelligent transportation systems,smart cities,and self-driving cars,providing a robust and scalable solution for future traffic management.展开更多
The increasing frequency of offshore engineering activities,particularly the expansion of offshore oil transport and the rise in the number of oil platforms,has greatly increased the potential risk of marine oil spill...The increasing frequency of offshore engineering activities,particularly the expansion of offshore oil transport and the rise in the number of oil platforms,has greatly increased the potential risk of marine oil spill incidents.Historically,several large oil spills have had long-term adverse effects on marine ecosystems and economic development,highlighting the importance of accurate-ly delineating and monitoring oil spill areas.In this study,graph neural network technology is introduced to implement semantic seg-mentation of SAR images,and two graph neural network models based on Graph-FCN and Graph-DeepLabV3+with the introduction of an attention mechanism are constructed and evaluated to improve the accuracy and efficiency of oil spill detection.By com-paring the Swin-Unet model,the Graph-DeepLabV3+model performs better in complex scenarios,especially in edge detail recognition.This not only provides strong technical support for marine oil spill monitoring but also provides an effective solution to deal with the potential risks brought by the increase of marine engineering activities,which is of great practical significance as it helps to safeguard the health and sustainable development of marine ecosystems and reduce the economic losses.展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limite...Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.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
Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies.Despite notable progress,e...Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies.Despite notable progress,existing computational techniques struggle to represent the interplay between sequence-derived and structuredependent protein features.To overcome this limitation,we introduce GLM-EP,a unified framework that fuses protein language models with equivariant graph neural networks.Bymerging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations,GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification.Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional sequence-based and independent deep-learning methods,yielding higher F1 and AUROC outcomes.Component-wise analysis demonstrates that GCNII,EGNN,and the gated multi-head attention mechanism function in a complementary manner to encode complex molecular attributes.In summary,GLM-EP serves as a robust and efficient tool for bacteriophage genomic analysis and provides valuable methodological perspectives for the discovery of antibiotic-resistance therapeutic targets.The corresponding code repository is available at:https://github.com/MiJia-ID/GLM-EP(accessed on 01 November 2025).展开更多
Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The...Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.展开更多
The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manp...The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.展开更多
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the National Natural Science Foundation of China(Grant No.12272316).
文摘The adjoint method is widely used in gradient-based optimization with high-dimensional design variables.However,the cost of solving the adjoint equations in each iteration is comparable to that of solving the flow field,resulting in expensive computational costs.To improve the efficiency of solving adjoint equations,we propose a physics-constrained graph neural networks for solving adjoint equations,named ADJ-PCGN.ADJ-PCGN establishes a mapping relationship between flow characteristics and adjoint vector based on data,serving as a replacement for the computationally expensive numerical solution of adjoint equations.A physics-based graph structure and message-passing mechanism are designed to endow its strong fitting and generalization capabilities.Taking transonic drag reduction and maximum lift-drag ratio of the airfoil as examples,results indicate that ADJ-PCGN attains a similar optimal shape as the classical direct adjoint loop method.In addition,ADJ-PCGN demonstrates strong generalization capabilities across different mesh topologies,mesh densities,and out-of-distribution conditions.It holds the potential to become a universal model for aerodynamic shape optimization involving states,geometries,and meshes.
基金supported by the National Key Research and Development Program of China(2020YFB1005704).
文摘Since the advent of smart contracts,security vulnerabilities have remained a persistent challenge,compromsing both the reliability of contract execution and the overall stability of the virtual currency market.Consequently,the academic community has devoted increasing attention to these security risks.However,conventional approaches to vulnerability detection frequently exhibit limited accuracy.To address this limitation,the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks(GNNs).The proposedmethod first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts.These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities.For these high-risk contracts,symbolic execution is employed to perform fine-grained,path-level analysis,thereby improving overall detection precision.Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00249743).
文摘This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.
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(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.
基金supported by the National Natural Science Founion of China(U2241285).
文摘Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective measures,and designing civil defense engineering solutions.Current state-of-the-art methods face several issues:Experimental research is difficult and costly to implement,theoretical research is limited to simple geometries and lacks precision,and direct simulations require substantial computational resources.To address these challenges,this paper presents a data-driven method for predicting blast loads on building surfaces.This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant,significantly improving its applicability in complex scenarios.This study introduces an innovative encoder-decoder graph neural network model named BlastGraphNet,which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events.The model also facilitates related downstream applications,such as damage mode identification and rapid assessment of virtual city explosions.The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%,and its inference speed is 3-4 orders of magnitude faster than that of state-of-the-art numerical methods.In extreme test cases involving buildings with complex geometries and building clusters,the method achieved high accuracy and excellent generalizability.The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.
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(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 the National Natural Science Foundation of China(Nos.62176122 and 62061146002).
文摘Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.
基金funded by the Key Research and Development Program of Zhejiang Province No.2023C01141the Science and Technology Innovation Community Project of the Yangtze River Delta No.23002410100suported by the Open Research Fund of the State Key Laboratory of Blockchain and Data Security,Zhejiang University.
文摘Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.
文摘Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.However,physiological recordings often exhibit irregular and unordered patterns,posing a significant challenge in conceptualising them as matrices.As a result,GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system.Additionally,our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain.Subsequently,demographic data are included,and a multi-task learning architecture is devised,integrating classification and regression tasks.The trials used an authentic dataset,including 800 brain x-ray pictures,consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases.Based on empirical evidence,our methodology demonstrates superior performance in classification,surpassing other comparison methods with a notable achievement of 92.27%in terms of area under the curve as well as a correlation coefficient of 0.62.
基金Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01641)。
文摘Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road occupancy and vehicle density.Therefore,the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment.Conventional prediction systems face difficulties in identifying highly congested areas,which leads to reduced prediction accuracy.The problem is addressed by integrating Graph Neural Networks(GNN)with the Lion Swarm Optimization(LSO)framework to tackle the congestion prediction problem.Initially,the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality.Then,the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment.From the connectivity and node relationship graph,modeling improves the overall prediction accuracy.During the analysis,the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies,which helps predict high congestion on roads with minimal deviation errors.There are three core optimization phases:roaming,hunting,and migration,which enable the framework to make dynamic adjustments to enhance the predictions.The framework’s efficacy is evaluated using benchmark datasets,where the proposed work achieves 99.2%accuracy and minimizes the prediction deviation value by up to 2.5%compared to other methods.With the new framework,there was a more accurate prediction of realtime congestion,lower computational cost,and improved regulation of traffic flow.This system is easily implemented in intelligent transportation systems,smart cities,and self-driving cars,providing a robust and scalable solution for future traffic management.
基金supported by the Natural Science Foun-dation of Shandong Province,China(No.ZR2024QF057)the Natural Science Foundation of Jiangsu Province,China(No.BK20240937)+1 种基金the Natural Science Foundation of China(No.42276215)the China University of Mining and Technology(CUMT)Open Sharing Fund for Large-Scale Instruments and Equipment(No.DYGX-2024-86).
文摘The increasing frequency of offshore engineering activities,particularly the expansion of offshore oil transport and the rise in the number of oil platforms,has greatly increased the potential risk of marine oil spill incidents.Historically,several large oil spills have had long-term adverse effects on marine ecosystems and economic development,highlighting the importance of accurate-ly delineating and monitoring oil spill areas.In this study,graph neural network technology is introduced to implement semantic seg-mentation of SAR images,and two graph neural network models based on Graph-FCN and Graph-DeepLabV3+with the introduction of an attention mechanism are constructed and evaluated to improve the accuracy and efficiency of oil spill detection.By com-paring the Swin-Unet model,the Graph-DeepLabV3+model performs better in complex scenarios,especially in edge detail recognition.This not only provides strong technical support for marine oil spill monitoring but also provides an effective solution to deal with the potential risks brought by the increase of marine engineering activities,which is of great practical significance as it helps to safeguard the health and sustainable development of marine ecosystems and reduce the economic losses.
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
基金supported by the National Key R&D Program of China(2022YFA1305700 to Li J)the“Dawn”Program of Shanghai Education Commission,China(21SG33 to Li J)The National Natural Science Foundation of China(82272793 to Gao X).
文摘Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.
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.
基金supported in part by funds from the Ministry of Science and Technology(2022FY101104).
文摘Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies.Despite notable progress,existing computational techniques struggle to represent the interplay between sequence-derived and structuredependent protein features.To overcome this limitation,we introduce GLM-EP,a unified framework that fuses protein language models with equivariant graph neural networks.Bymerging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations,GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification.Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional sequence-based and independent deep-learning methods,yielding higher F1 and AUROC outcomes.Component-wise analysis demonstrates that GCNII,EGNN,and the gated multi-head attention mechanism function in a complementary manner to encode complex molecular attributes.In summary,GLM-EP serves as a robust and efficient tool for bacteriophage genomic analysis and provides valuable methodological perspectives for the discovery of antibiotic-resistance therapeutic targets.The corresponding code repository is available at:https://github.com/MiJia-ID/GLM-EP(accessed on 01 November 2025).
基金supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2022LL.Z012 and ZR2021LLZ001)the Key Research and Development Program of Shandong Province,China(Grant No.2023CXGC010901).
文摘Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.
基金The authors would like to express appreciation to the National Natural Science Foundation of China(Grant No.61762067)for their financial support.
文摘The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.
