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
Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-...Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.展开更多
Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate ...Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.展开更多
Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.The k-truss is one of the most commonly studied co...Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.The k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a k-truss.Existing works mostly focus on truss computation in static graphs by sequential models.However,the graphs are constantly changing dynamically in the real world.We study distributed truss computation in dynamic graphs in this paper.In particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed model.Iteratively decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition algorithm.Moreover,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the computation.Extensive experiments have been conducted to show the scalability and efficiency of the proposed algorithm.展开更多
Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information ...Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology learning.At the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network effectively.To address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs.Firstly,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the nodes.Secondly,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic graphs.Specifically,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention mechanism.Finally,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and position.Experimental results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting.展开更多
Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in S...Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting.However,these methods do not adapt well to dynamic seasonal variations in wave data.In this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural network.This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion.First,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern perspectives.Second,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics.Finally,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories.The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction.Furthermore,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient appro...The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.展开更多
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause o...Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.展开更多
Program slice has many applications such as program debugging, testing, maintenance, and complexity measurement. A static slice consists of all statements in program P that may effect the value of variable v a...Program slice has many applications such as program debugging, testing, maintenance, and complexity measurement. A static slice consists of all statements in program P that may effect the value of variable v at some point p , and a dynamic slice consists only of statements that influence the value of variable occurrence for specific program inputs. In this paper, we concern the problem of dynamic slicing of object oriented programs which, to our knowledge, has not been addressed in the literatures. To solve this problem, we present the dynamic object oriented dependence graph (DODG)which is an arc classified digraph to explicitly represent various dynamic dependence between statement instances for a particular execution of an object oriented program. Based on the DODG, we present a two phase backward algorithm for computing a dynamic slice of an object oriented program.展开更多
Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The ...Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators.Existing methods often fail to capture the dynamic interactions between health indicators over time,resulting in limited predictive accuracy.To address these challenges,we propose a novel framework,Dynamic Graph Learning with Spatial-Temporal Fusion Attention(DGL-STFA),which transforms health indicator series time-data into time-evolving graph representations.The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns,a self-attention mechanism to construct dynamic adjacency matrices that adapt over time,and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation.This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies,enhancing SOH prediction accuracy.Extensive experiments were conducted on the NASA and CALCE battery datasets,comparing this framework with traditional time-series prediction methods and other graph-based prediction methods.The results demonstrate that our framework significantly improves prediction accuracy,with a mean absolute error more than 30%lower than other methods.Further analysis demonstrated the robustness of DGL-STFA across various battery life stages,including early,mid,and end-of-life phases.These results highlight the capability of DGL-STFA to accurately predict SOH,addressing critical challenges in advancing battery health monitoring for energy storage applications.展开更多
Emotional electroencephalography(EEG)signals are a primary means of recording emotional brain activity.Currently,the most effective methods for analyzing emotional EEG signals involve feature engineering and neural ne...Emotional electroencephalography(EEG)signals are a primary means of recording emotional brain activity.Currently,the most effective methods for analyzing emotional EEG signals involve feature engineering and neural networks.However,neural networks possess a strong ability for automatic feature extraction.Is it possible to discard feature engineering and directly employ neural networks for end-to-end recognition?Based on the characteristics of EEG signals,this paper proposes an end-to-end feature extraction and classification method for a dynamic self-attention network(DySAT).The study reveals significant differences in brain activity patterns associated with different emotions across various experimenters and time periods.The results of this experiment can provide insights into the reasons behind these differences.展开更多
Graph neural networks(GNNs)have emerged as a powerful tool for effectively mining and learning from graphstructured data,with applications spanning numerous domains.However,most research focuses on static graphs,negle...Graph neural networks(GNNs)have emerged as a powerful tool for effectively mining and learning from graphstructured data,with applications spanning numerous domains.However,most research focuses on static graphs,neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time.By integrating sequence modeling modules into traditional GNN architectures,dynamic GNNs aim to bridge this gap,capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks.This paper provides a comprehensive review of the fundamental concepts,key techniques,and stateof-the-art dynamic GNN models.We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated.We also discuss large-scale dynamic GNNs and pre-training techniques.Although dynamic GNNs have shown superior performance,challenges remain in scalability,handling heterogeneous information,and lack of diverse graph datasets.The paper also discusses possible future directions,such as adaptive and memory-enhanced models,inductive learning,and theoretical analysis.展开更多
Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making i...Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making it increasingly reliant on prior knowledge in this context.In this paper,we introduce a radiology report generation network termed Dynamics Priori Networks(DPN),which leverages a dynamic knowledge graph and prior knowledge.Concretely,we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s intelligence.Notably,we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results.Our method is evaluated on two widely available datasets:X-ray collection from Indiana University(IU X-ray)and Medical Information Mart for Intensive Care,Chest X-Ray(MIMIC-CXR),where it demonstrates superior performance,particularly excelling in critical metrics.展开更多
Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus o...Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in applications.In most real-world scena...Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in applications.In most real-world scenarios,entities and their relationships are subject to constant changes.Graphs that record such changes are called dynamic graphs.In recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics results.As the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing systems.With the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing tasks.GPU-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph computing.To comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph computing.In addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.展开更多
Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservat...Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.展开更多
Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications...Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.展开更多
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co-supported by the National Key Research and Development Program of China(No. 2021YFB3301504)the National Natural Science Foundation of China (Nos. 62072415, 62036010, 42301526, 62372416 and 62472389)the National Natural Science Foundation of Henan Province, China (No. 242300421215)
文摘Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.
基金supported by the Natural Science Foundation of China(No.U22A2099)the Innovation Project of Guangxi Graduate Education(YCBZ2023130).
文摘Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.
基金supported in part by the National Key Research and Development Program of China(No.2020YFB1005900)in part by National Natural Science Foundation of China(No.62122042)in part by Shandong University Multidisciplinary Research and Innovation Team of Young Scholars(No.2020QNQT017)。
文摘Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.The k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a k-truss.Existing works mostly focus on truss computation in static graphs by sequential models.However,the graphs are constantly changing dynamically in the real world.We study distributed truss computation in dynamic graphs in this paper.In particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed model.Iteratively decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition algorithm.Moreover,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the computation.Extensive experiments have been conducted to show the scalability and efficiency of the proposed algorithm.
文摘Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology learning.At the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network effectively.To address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs.Firstly,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the nodes.Secondly,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic graphs.Specifically,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention mechanism.Finally,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and position.Experimental results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting.
基金The National Key R&D Program of China under contract No.2021YFC3101604。
文摘Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting.However,these methods do not adapt well to dynamic seasonal variations in wave data.In this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural network.This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion.First,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern perspectives.Second,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics.Finally,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories.The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction.Furthermore,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
文摘The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.
基金supported by the Medical and Health Research Program of Zhejiang Province(No.2015KYB128)the Zhejiang Provincial Natural Science Foundation(No.LQ15H030004),China
文摘Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
文摘Program slice has many applications such as program debugging, testing, maintenance, and complexity measurement. A static slice consists of all statements in program P that may effect the value of variable v at some point p , and a dynamic slice consists only of statements that influence the value of variable occurrence for specific program inputs. In this paper, we concern the problem of dynamic slicing of object oriented programs which, to our knowledge, has not been addressed in the literatures. To solve this problem, we present the dynamic object oriented dependence graph (DODG)which is an arc classified digraph to explicitly represent various dynamic dependence between statement instances for a particular execution of an object oriented program. Based on the DODG, we present a two phase backward algorithm for computing a dynamic slice of an object oriented program.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4606200)Key Program of Science and Technology of Yunnan Province,China (No.202302AB080020)Key Project of Shanghai Zhangjiang National Independent hnovation Demonstration Zone,China(No.ZJ2021-2D-006).
文摘Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators.Existing methods often fail to capture the dynamic interactions between health indicators over time,resulting in limited predictive accuracy.To address these challenges,we propose a novel framework,Dynamic Graph Learning with Spatial-Temporal Fusion Attention(DGL-STFA),which transforms health indicator series time-data into time-evolving graph representations.The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns,a self-attention mechanism to construct dynamic adjacency matrices that adapt over time,and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation.This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies,enhancing SOH prediction accuracy.Extensive experiments were conducted on the NASA and CALCE battery datasets,comparing this framework with traditional time-series prediction methods and other graph-based prediction methods.The results demonstrate that our framework significantly improves prediction accuracy,with a mean absolute error more than 30%lower than other methods.Further analysis demonstrated the robustness of DGL-STFA across various battery life stages,including early,mid,and end-of-life phases.These results highlight the capability of DGL-STFA to accurately predict SOH,addressing critical challenges in advancing battery health monitoring for energy storage applications.
文摘Emotional electroencephalography(EEG)signals are a primary means of recording emotional brain activity.Currently,the most effective methods for analyzing emotional EEG signals involve feature engineering and neural networks.However,neural networks possess a strong ability for automatic feature extraction.Is it possible to discard feature engineering and directly employ neural networks for end-to-end recognition?Based on the characteristics of EEG signals,this paper proposes an end-to-end feature extraction and classification method for a dynamic self-attention network(DySAT).The study reveals significant differences in brain activity patterns associated with different emotions across various experimenters and time periods.The results of this experiment can provide insights into the reasons behind these differences.
基金supported in part by National Science and Technology Major Project(2022ZD0114802)by National Natural Science Foundation of China(Grant Nos.U2241212,61932001)+4 种基金by Beijing Natural Science Foundation(No.4222028)by Beijing Outstanding Young Scientist Program(No.BJJWZYJH012019100020098)by Alibaba Group through Alibaba Innovative Research Program,and by Huawei-Renmin University joint program on Information Retrievalsupport provided by the fund for building world-class universities(disciplines)of Renmin University of China,by Engineering Research Center of Next-Generation Intelligent Search and Recommendation,Ministry of Education,Intelligent Social Governance Interdisciplinary Platform,Major Innovation&Planning Interdisciplinary Platform for the“Double-First Class”Initiative,Public Policy and Decision-making Research Lab,and Public Computing Cloud,Renmin University of ChinaBeijing Key Laboratory of Big Data Management and Analysis Methods,MOE Key Lab of Data Engineering and Knowledge Engineering,and Pazhou Laboratory(Huangpu),Guangzhou,Guangdong 510555,China.
文摘Graph neural networks(GNNs)have emerged as a powerful tool for effectively mining and learning from graphstructured data,with applications spanning numerous domains.However,most research focuses on static graphs,neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time.By integrating sequence modeling modules into traditional GNN architectures,dynamic GNNs aim to bridge this gap,capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks.This paper provides a comprehensive review of the fundamental concepts,key techniques,and stateof-the-art dynamic GNN models.We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated.We also discuss large-scale dynamic GNNs and pre-training techniques.Although dynamic GNNs have shown superior performance,challenges remain in scalability,handling heterogeneous information,and lack of diverse graph datasets.The paper also discusses possible future directions,such as adaptive and memory-enhanced models,inductive learning,and theoretical analysis.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB38050100)the Shenzhen Science and Technology Program(No.SGDX20201103095603009)the Shenzhen Polytechnic Research Fund(No.6023310009K).
文摘Radiology report generation is of significant importance.Unlike standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making it increasingly reliant on prior knowledge in this context.In this paper,we introduce a radiology report generation network termed Dynamics Priori Networks(DPN),which leverages a dynamic knowledge graph and prior knowledge.Concretely,we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s intelligence.Notably,we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results.Our method is evaluated on two widely available datasets:X-ray collection from Indiana University(IU X-ray)and Medical Information Mart for Intensive Care,Chest X-Ray(MIMIC-CXR),where it demonstrates superior performance,particularly excelling in critical metrics.
基金supported by the National Natural Science Foundation of China(grant no.61573328).
文摘Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by the Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
基金National Natural Science Foundation of China(Grant Nos.61972444,61825202,62072195,and 61832006)Zhejiang Lab(2022P10AC02).
文摘Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in applications.In most real-world scenarios,entities and their relationships are subject to constant changes.Graphs that record such changes are called dynamic graphs.In recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics results.As the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing systems.With the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing tasks.GPU-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph computing.To comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph computing.In addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.
基金supported in part by the Science and Technology Innovation Program of Hunan Province(No.2022RC1090)in part by the National Natural Science Foundation of China(No.62173349)+2 种基金in part by the Natural Science Foundation of Hunan Province(No.2022J20076)in part by the Innovation Driven Projection of Central South University(No.2023CXQD073)in part by the Major Program of Xiangjiang Laboratory(No.22XJ01005).
文摘Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.
基金supported by 2022 Shenyang Philosophy and Social Science Planning under grant SY202201Z,Liaoning Provincial Department of Education Project under grant LJKZ0588.
文摘Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.
