94 results on '"graph structure learning"'
Search Results
2. A novel spatial–temporal graph convolution network based on temporal embedding graph structure learning for multivariate time series prediction
- Author
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Lei, Tianyang, Li, Jichao, Yang, Kewei, and Gong, Chang
- Published
- 2025
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3. Honest-GE: 2-step heuristic optimization and node-level embedding empower spatial-temporal graph model for ECG
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Zhang, Huaicheng, Liu, Wenhan, Luo, Deyu, Shi, Jiguang, Guo, Qianxi, Ge, Yue, Chang, Sheng, Wang, Hao, He, Jin, and Huang, Qijun
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- 2024
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4. Health insurance fraud detection based on multi-channel heterogeneous graph structure learning
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Hong, Binsheng, Lu, Ping, Xu, Hang, Lu, Jiangtao, Lin, Kaibiao, and Yang, Fan
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- 2024
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5. Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.
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Nie, Wan, Yu, Yingying, Wang, Xueying, Wang, Ruohan, and Li, Shuai Cheng
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REGULATOR genes , *PROTEIN-tyrosine kinases , *TRANSCRIPTOMES , *CELLULAR signal transduction , *BREAST cancer - Abstract
Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell‐cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low‐dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial‐to‐mesenchymal transition and PI3K/AKT signaling in specific sub‐regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder‐based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes. [ABSTRACT FROM AUTHOR]
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- 2024
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6. scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data.
- Author
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Yuan, Lin, Zhao, Ling, Jiang, Yufeng, Shen, Zhen, Zhang, Qinhu, Zhang, Ming, Zheng, Chun-Hou, and Huang, De-Shuang
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MACHINE learning , *DEEP learning , *RNA sequencing , *CELL lines , *CELL anatomy - Abstract
The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)–based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network.
- Author
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Fan, Yuxin, Fu, Tingting, Listopad, Nikolai Izmailovich, Liu, Peng, Garg, Sahil, and Hassan, Mohammad Mehedi
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CONVOLUTIONAL neural networks ,GRAPH neural networks ,ANOMALY detection (Computer security) ,INTERNET of things ,TIME series analysis - Abstract
The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network
- Author
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Yuxin Fan, Tingting Fu, Nikolai Izmailovich Listopad, Peng Liu, Sahil Garg, and Mohammad Mehedi Hassan
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Industrial Internet of Things ,Graph structure learning ,Gated graph attention network ,Temporal convolutional network ,Anomaly detection ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection.
- Published
- 2024
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9. Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics
- Author
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Wan Nie, Yingying Yu, Xueying Wang, Ruohan Wang, and Shuai Cheng Li
- Subjects
graph structure learning ,cell‐cell interactions ,spatial clustering ,spatial transcriptomics ,Science - Abstract
Abstract Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell‐cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low‐dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial‐to‐mesenchymal transition and PI3K/AKT signaling in specific sub‐regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder‐based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.
- Published
- 2024
- Full Text
- View/download PDF
10. Gmad: multivariate time series anomaly detection based on graph matching learning
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Kong, Jun, Wang, Kang, Jiang, Min, and Tao, Xuefeng
- Published
- 2024
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11. Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow Forecasting.
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Chai, Wenguang, Luo, Qingfeng, Lin, Zhizhe, Yan, Jingwen, Zhou, Jinglin, and Zhou, Teng
- Abstract
Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with urbanization worsening traffic congestion, which affects daily life, economic growth, and the environment. Precise forecasts aid in managing and optimizing transportation systems, reducing congestion, and improving air quality by cutting emissions. However, predicting outcomes is difficult due to intricate spatial relationships, nonlinear temporal patterns, and the challenges associated with long-term forecasting. Current research often uses static graph structures, overlooking dynamic and long-range dependencies. To tackle these issues, we introduce the spatiotemporal dynamic multi-hop network (ST-DMN), a Seq2Seq framework. This model incorporates spatiotemporal convolutional blocks (ST-Blocks) with residual connections in the encoder to condense historical traffic data into a fixed-dimensional vector. A dynamic graph represents time-varying inter-segment relationships, and multi-hop operation in the encoder's spatial convolutional layer and the decoder's diffusion multi-hop graph convolutional gated recurrent units (DMGCGRUs) capture long-range dependencies. Experiments on two real-world datasets METR-LA and PEMS-BAY show that ST-DMN surpasses existing models in three metrics. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. SGK-Net: A Novel Navigation Scene Graph Generation Network.
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Yang, Wenbin, Qiu, Hao, Luo, Xiangfeng, and Xie, Shaorong
- Subjects
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NAVIGATION in shipping , *RESEARCH vessels , *COMPUTATIONAL complexity , *MULTIMODAL user interfaces - Abstract
Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation. [ABSTRACT FROM AUTHOR]
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- 2024
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13. MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting.
- Author
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Qiu, Mingjie, Tan, Zhiyi, and Bao, Bing-Kun
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GRAPH neural networks ,MULTISCALE modeling ,COMMUNICABLE diseases ,COVID-19 pandemic ,LEARNING modules - Abstract
Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) current models broaden receptive fields by scaling the depth of GNNs, which is insufficient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic patterns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific patterns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result. Code is available at https://github.com/JashinKorone/MSGNN. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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14. MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction.
- Author
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Junpeng Lin, Binsheng Hong, Zhongqi Cai, Ping Lu, and Kaibiao Lin
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GRAPH neural networks ,DRUG interactions ,DRUG discovery ,MOLECULAR graphs - Abstract
Accurately predicting Drug-Drug Interaction (DDI) is a critical and challenging aspect of the drug discovery process, particularly in preventing adverse reactions in patients undergoing combination therapy. However, current DDI prediction methods often overlook the interaction information between chemical substructures of drugs, focusing solely on the interaction information between drugs and failing to capture sufficient chemical substructure details. To address this limitation, we introduce a novel DDI prediction method: Multilayer Adaptive Soft Mask Graph Neural Network (MASMDDI). Specifically, we first design a multi-layer adaptive soft mask graph neural network to extract substructures from molecular graphs. Second, we employ an attention mechanism to mine substructure feature information and update latent features. In this process, to optimize the final feature representation, we decompose drug-drug interactions into pairwise interaction correlations between the core substructures of each drug. Third, we use these features to predict the interaction probabilities of DDI tuples and evaluate the model using real-world datasets. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods in DDI prediction. Furthermore, MASMDDI exhibits excellent performance in predicting DDIs of unknown drugs in two tasks that are more aligned with real-world scenarios. In particular, in the transductive scenario using the DrugBank dataset, the ACC and AUROC and AUPRC scores of MASMDDI are 0.9596, 0.9903, and 0.9894, which are 2% higher than the best performing baseline. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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15. Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution.
- Author
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Sun, Chenchen, Ning, Yan, Shen, Derong, and Nie, Tiezheng
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GRAPH neural networks ,FORECASTING ,GRAPH algorithms - Abstract
An accurate short-term load forecasting plays an important role in modern power system's operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. Graph Transformer Network Incorporating Sparse Representation for Multivariate Time Series Anomaly Detection.
- Author
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Yang, Qian, Zhang, Jiaming, Zhang, Junjie, Sun, Cailing, Xie, Shanyi, Liu, Shangdong, and Ji, Yimu
- Subjects
ANOMALY detection (Computer security) ,TRANSFORMER models ,CYBER physical systems ,SPARSE graphs ,CYBERTERRORISM ,DEEP learning - Abstract
Cyber–physical systems (CPSs) serve as the pivotal core of Internet of Things (IoT) infrastructures, such as smart grids and intelligent transportation, deploying interconnected sensing devices to monitor operating status. With increasing decentralization, the surge in sensor devices expands the potential vulnerability to cyber attacks. It is imperative to conduct anomaly detection research on the multivariate time series data that these sensors produce to bolster the security of distributed CPSs. However, the high dimensionality, absence of anomaly labels in real-world datasets, and intricate non-linear relationships among sensors present considerable challenges in formulating effective anomaly detection algorithms. Recent deep-learning methods have achieved progress in the field of anomaly detection. Yet, many methods either rely on statistical models that struggle to capture non-linear relationships or use conventional deep learning models like CNN and LSTM, which do not explicitly learn inter-variable correlations. In this study, we propose a novel unsupervised anomaly detection method that integrates Sparse Autoencoder with Graph Transformer network (SGTrans). SGTrans leverages Sparse Autoencoder for the dimensionality reduction and reconstruction of high-dimensional time series, thus extracting meaningful hidden representations. Then, the multivariate time series are mapped into a graph structure. We introduce a multi-head attention mechanism from Transformer into graph structure learning, constructing a Graph Transformer network forecasting module. This module performs attentive information propagation between long-distance sensor nodes and explicitly models the complex temporal dependencies among them to enhance the prediction of future behaviors. Extensive experiments and evaluations on three publicly available real-world datasets demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. A Structure-Enhanced Heterogeneous Graph Representation Learning with Attention-Supplemented Embedding Fusion.
- Author
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Pham, Phu
- Subjects
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REPRESENTATIONS of graphs , *GRAPH neural networks , *SCIENTIFIC community , *DATA mining , *INFORMATION networks , *LATENT variables - Abstract
In recent years, heterogeneous network/graph representation learning/embedding (HNE) has drawn tremendous attentions from research communities in multiple disciplines. HNE has shown its outstanding performances in various networked data analysis and mining tasks. In fact, most of real-world information networks in multiple fields can be modelled as the heterogeneous information networks (HIN). Thus, the HNE-based techniques can sufficiently capture rich-structured and semantic latent features from the given information network in order to facilitate for different task-driven learning tasks. This is considered as fundamental success of HNE-based approach in comparing with previous traditional homogeneous network/graph based embedding techniques. However, there are recent studies have also demonstrated that the heterogeneous network/graph modelling and embedding through graph neural network (GNN) is not usually reliable. This challenge is original come from the fact that most of real-world heterogeneous networks are considered as incomplete and normally contain a large number of feature noises. Therefore, multiple attempts have proposed recently to overcome this limitation. Within this approach, the meta-path-based heterogeneous graph-structured latent features and GNN-based parameters are jointly learnt and optimized during the embedding process. However, this integrated GNN and heterogeneous graph structure (HGS) learning approach still suffered a challenge of effectively parameterizing and fusing different graph-structured latent features from both GNN- and HGS-based sides into better task-driven friendly and noise-reduced embedding spaces. Therefore, in this paper we proposed a novel attention-supplemented heterogeneous graph structure embedding approach, called as: AGSE. Our proposed AGSE model supports to not only achieve the combined rich heterogeneous structural and GNN-based aggregated node representations but also transform achieved node embeddings into noise-reduced and task-driven friendly embedding space. Extensive experiments in benchmark heterogeneous networked datasets for node classification task showed the effectiveness of our proposed AGSE model in comparing with state-of-the-art network embedding baselines. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. Ultra-Short-Term Power Prediction of Large Offshore Wind Farms Based on Spatiotemporal Adaptation of Wind Turbines.
- Author
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An, Yuzheng, Zhang, Yongjun, Lin, Jianxi, Yi, Yang, Fan, Wei, and Cai, Zihan
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OFFSHORE wind power plants ,WIND power plants ,CONVOLUTIONAL neural networks ,WIND turbines ,WIND power ,FEATURE extraction - Abstract
Accurately predicting the active power output of offshore wind power is of great significance for reducing the uncertainty in new power systems. By utilizing the spatiotemporal correlation characteristics among wind turbine unit outputs, this paper embeds the Diffusion Convolutional Neural Network (DCNN) into the Gated Recurrent Unit (GRU) for the feature extraction of spatiotemporal correlations in wind turbine unit outputs. It also combines graph structure learning to propose a sequence-to-sequence model for ultra-short-term power prediction in large offshore wind farms. Firstly, the electrical connection graph within the wind farm is used to preliminarily determine the reference adjacency matrix for the wind turbine units within the farm, injecting prior knowledge of the adjacency matrix into the model. Secondly, a convolutional neural network is utilized to convolve the historical curves of units within the farm along the time dimension, outputting a unit connection probability vector. The Gumbel–softmax reparameterization method is then used to make the probability vector differentiable, thereby generating an optimal adjacency matrix for the prediction task based on the probability vector. At the same time, the difference between the two adjacency matrices is added as a regularization term to the loss function to reduce model overfitting. The simulation of actual cases shows that the proposed model has good predictive performance in ultra-short-term power prediction for large offshore wind farms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. A novel autism spectrum disorder identification method: spectral graph network with brain-population graph structure joint learning.
- Author
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Li, Sihui, Li, Duo, Zhang, Rui, and Cao, Feilong
- Abstract
Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition. Its early and accurate diagnosis is critical in enhancing the quality of life for affected individuals. Graph neural networks supply promising approaches for ASD diagnosis. However, existing works typically focus on brain-level or population-level classification methods, where the former usually disregards subjects' non-imaging information and inter-subject relationships, and the latter generally fails to adequately evaluate and detect disease-associated brain regions and biomarkers. Furthermore, relatively static graph structures and shallow network architectures hinder the abundant extraction of information, affecting the performance of ASD identification. Accordingly, this paper proposes a new spectral graph network with brain-population graph structure joint learning (BPGLNet) for ASD diagnosis. This new framework involves two main components. Firstly, a brain-level graph learning module is designed to acquire valuable features of brain regions and identify effective biomarkers for each subject. In particular, it constructs a brain-aware representation learning network by fusing an improved graph pooling strategy and spectral graph convolution to learn subgraph structures and features of brain regions. Subsequently, based on these generated features, a population-level graph learning module is developed to capture relationships between different subjects. It builds an adaptive edge generator network by integrating non-imaging and imaging data, forming a learnable population graph. Further, this module also devises a deep cascade spectral graph network to enrich high-level feature representation of data and complete ASD identification. Experiments on the benchmark dataset reveal the state-of-the-art performance of BPGLNet. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. AMGCN: adaptive multigraph convolutional networks for traffic speed forecasting.
- Author
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Li, Chenghao, Zhao, Yahui, and Zhang, Zhenguo
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TRAFFIC speed ,TRAFFIC estimation ,GRAPH neural networks ,MULTIGRAPH ,TIME series analysis - Abstract
Traffic speed forecasting is a crucial aspect of traffic management that requires an accurate multi spatiotemporal time series forecasting technique. Previous studies typically employ graph neural network (GNN)-based methods for this task, but they are limited by their focus on spatial dependence based on real geographic distance in road networks. These structures are often inadequate for accurately describing spatial dependencies in the real world. Recently, multigraph neural networks (MGNNs) have shown considerable promise for improving forecasting performance by modelling graph structures from different spatial relationships. However, these kinds of methods do not account for complex relationships between aspects and latent dependence that cannot be known beforehand. To address these shortcomings, we propose a novel traffic speed forecasting method called adaptive multigraph convolutional networks (AMGCN), where we first introduce five predefined graphs based on spatial distance, accessibility, pattern similarity, distribution similarity and KL divergence. We fuse these graphs into a complex prior graph using a method based on spatial attention and graph relation attention. In this process, the spatial dependence in the road network is modelled comprehensively from multiple perspectives. In addition, we introduce the adaptive graph to calculate the similarity between learnable node embeddings to assist the forecasting. In this process, spatial dependencies that still cannot be captured by predefined graphs can be obtained by the way of data-driven. We utilize a mix-hop graph convolution with a residual connection to capture spatial dependencies in prior graphs and adaptive graphs. Time dependencies are also captured through causal convolution based on equidistance downsampling to prevent overfitting and redundancy in capturing spatiotemporal interactions. Extensive experiments on four real-world datasets demonstrate that our proposed method achieves superior performance compared to other baselines and effectively captures the spatiotemporal dependencies of the road network. Source codes are available at https://github.com/hfimmortal/AMGCN. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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21. Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting.
- Author
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Bikram, Pritam, Das, Shubhajyoti, and Biswas, Arindam
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GRAPH neural networks ,TRAFFIC estimation ,ARTIFICIAL neural networks ,TRAFFIC speed ,TRAFFIC flow ,DEEP learning ,TOPOLOGICAL entropy - Abstract
A smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving unwrinkled traffic flow. However, due to its spatial and temporal interdependence, traffic forecasting might be more difficult. Graph neural networks (GNN) are widely used to obtain traffic forecasting due to their capability to operate in non-Euclidean data. The topological connection of the traffic network plays a crucial role in graph structure learning. Therefore, the importance of the adjacency matrix in graph construction might be significant for effective traffic forecasting. As a result, for efficient graph construction, a Deep Spatial-Temporal Graph Neural Network (DSTGNN) is proposed for the construction of the weighted adjacency matrix and traffic speed prediction accurately by recording topological and temporal information. Three different weighted adjacency matrices are suggested depending on three different proposed algorithms for different variations of traffic's spatial condition. The new adjacency matrix is used in a novel DSTGNN for predicting the traffic state. The novel model comprised multiple layers with skip connections to adequately extract the variation of temporal and spatial information. DSTGNN outperforms the standard and other graph neural network models on four traffic speed datasets, SZ-taxi, Los-loop, PeMSD7, and METR-LA. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Health insurance fraud detection based on multi-channel heterogeneous graph structure learning
- Author
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Binsheng Hong, Ping Lu, Hang Xu, Jiangtao Lu, Kaibiao Lin, and Fan Yang
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Health insurance ,Fraud detection ,Heterogeneous graph neural networks ,Graph convolutional network ,Graph structure learning ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.
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- 2024
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23. Multikernel Graph Structure Learning for Multispectral Point Cloud Classification
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Qingwang Wang, Zifeng Zhang, Jiangbo Huang, Tao Shen, and Yanfeng Gu
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Graph structure learning ,multiple kernel learning ,multispectral LiDAR data ,point cloud classification ,prior constraint ,Ocean engineering ,TC1501-1800 ,Geophysics. Cosmic physics ,QC801-809 - Abstract
Multispectral point cloud, with spatial and multiple-band spectral information, provides the data basis for finer land cover 3-D classification. However, spectral information is not well utilized by traditional methods of point cloud classification. Benefiting from the excellent performance of graph neural networks on non-Euclidean data, it is well suited to the joint use of spatial and spectral information from multispectral point clouds to achieve better classification performance. However, existing graph-based methods for point cloud classification rely on manual experience to construct input graph and cannot adapt to the complexity of remote sensing scenes. In this article, we propose a novel multikernel graph structure learning (MKGSL) framework for multispectral point cloud classification. Specifically, we explore the high-dimensional feature distribution properties of multispectral point clouds in Hilbert space through the use of kernel method. An innovative multiple-kernel learning mechanism is embedded into our network, which allows to obtain better mappings adaptively. Simultaneously, a series of prior constraints designed based on land cover distribution characteristics are imposed on the network training process, which leads the learned graph of the multispectral point cloud to facilitate better classification. Our method is dedicated to adaptively constructing task-oriented graph structures to improve the performance of multispectral point cloud classification. Experimental comparisons demonstrate that the proposed MKGSL performs better than several state-of-the-art methods on two real multispectral point cloud datasets.
- Published
- 2024
- Full Text
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24. Learnable Brain Connectivity Structures for Identifying Neurological Disorders
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Zhengwang Xia, Tao Zhou, Zhuqing Jiao, and Jianfeng Lu
- Subjects
Deep learning ,graph neural network ,graph structure learning ,brain disorder identification ,Medical technology ,R855-855.5 ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. However, a key limitation of these models is that the inputs, namely brain networks/graphs, are constructed using predefined statistical metrics (e.g., Pearson correlation) and are not learnable. The lack of learnability restricts the flexibility of these approaches. While statistically-specific brain networks can be highly effective in recognizing certain diseases, their performance may not exhibit robustness when applied to other types of brain disorders. To address this issue, we propose a novel module called Brain Structure Inference (termed BSI), which can be seamlessly integrated with multiple downstream tasks within a unified framework, enabling end-to-end training. It is highly flexible to learn the most beneficial underlying graph structures directly for specific downstream tasks. The proposed method achieves classification accuracies of 74.83% and 79.18% on two publicly available datasets, respectively. This suggests an improvement of at least 3% over the best-performing existing methods for both tasks. In addition to its excellent performance, the proposed method is highly interpretable, and the results are generally consistent with previous findings.
- Published
- 2024
- Full Text
- View/download PDF
25. Survey of Research on Personalized News Recommendation Approaches
- Author
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MENG Xiangfu, HUO Hongjin, ZHANG Xiaoyan, WANG Wanchun, ZHU Jinxia
- Subjects
personalized news recommendation ,deep learning ,graph structure learning ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Personalized news recommendation is an important technology to help users obtain the news information they are interested in and alleviate information overload. In recent years, with the development of information technology and society, personalized news recommendation has been increasingly widely studied, and has achieved remarkable success in improving the news reading experience of users. This paper aims to systematically summarize personalized news recommendation methods based on deep learning. Firstly, it introduces personalized news recommendation methods and analyzes their characteristics and influencing factors. Then, the overall framework of personalized news recommendation is given, and the personalized news recommendation methods based on deep learning are analyzed and summarized. On this basis, it focuses on personalized news recommendation methods based on graph structure learning, including user-news interaction graph, knowledge graph and social relationship graph. Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the future research direction.
- Published
- 2023
- Full Text
- View/download PDF
26. Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution
- Author
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Chenchen Sun, Yan Ning, Derong Shen, and Tiezheng Nie
- Subjects
Short-term load forecasting ,Graph structure learning ,Graph neural networks ,Dilated 1D-CNN ,Temporal dependencies ,Spatial dependencies ,Information technology ,T58.5-58.64 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.
- Published
- 2023
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27. A Survey of Personalized News Recommendation
- Author
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Xiangfu Meng, Hongjin Huo, Xiaoyan Zhang, Wanchun Wang, and Jinxia Zhu
- Subjects
Personalized news recommendation ,Prediction models ,News ranking and display ,Graph structure learning ,Information technology ,T58.5-58.64 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.
- Published
- 2023
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28. Learnable Brain Connectivity Structures for Identifying Neurological Disorders.
- Author
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Xia, Zhengwang, Zhou, Tao, Jiao, Zhuqing, and Lu, Jianfeng
- Subjects
GRAPH neural networks ,ARTIFICIAL neural networks ,LARGE-scale brain networks ,DEEP learning ,PEARSON correlation (Statistics) - Abstract
Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. However, a key limitation of these models is that the inputs, namely brain networks/graphs, are constructed using predefined statistical metrics (e.g., Pearson correlation) and are not learnable. The lack of learnability restricts the flexibility of these approaches. While statistically-specific brain networks can be highly effective in recognizing certain diseases, their performance may not exhibit robustness when applied to other types of brain disorders. To address this issue, we propose a novel module called Brain Structure Inference (termed BSI), which can be seamlessly integrated with multiple downstream tasks within a unified framework, enabling end-to-end training. It is highly flexible to learn the most beneficial underlying graph structures directly for specific downstream tasks. The proposed method achieves classification accuracies of 74.83% and 79.18% on two publicly available datasets, respectively. This suggests an improvement of at least 3% over the best-performing existing methods for both tasks. In addition to its excellent performance, the proposed method is highly interpretable, and the results are generally consistent with previous findings. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Multi-node system modeling and monitoring with extended directed graphical models.
- Author
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Li, Dengyu and Wang, Kaibo
- Subjects
MANUFACTURING processes ,STATISTICAL correlation ,PARAMETER estimation ,COMPLEX variables ,MULTIAGENT systems ,MATRIX functions - Abstract
Complex manufacturing systems usually contain a large number of variables. Dominated by certain engineering mechanisms, these variables show complicated relationships that cannot be effectively expressed by simple correlation matrices or functions, thus increasing the difficulty of modeling and monitoring these systems. The directed graphical model (DGM) has been used as a flexible tool for describing the relationship among variables in complex systems. However, the DGM treats all variables equally and fails to consider the structural information among them that usually exists. To address this problem, an extended directed graphical model (EDGM) and related parameter estimation, monitoring, and structure learning methods are proposed in this work. Taking prior engineering knowledge into consideration, the EDGM assigns variables into groups and uses groups of variables as nodes in the graph model. By adding hidden state variables to each node, the EDGM can effectively represent the relationship within and between nodes and provide promising monitoring performance. Numerical experiments and a real-world case study of the monocrystalline silicon growth process are performed to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
30. A Survey of Personalized News Recommendation.
- Author
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Meng, Xiangfu, Huo, Hongjin, Zhang, Xiaoyan, Wang, Wanchun, and Zhu, Jinxia
- Subjects
KNOWLEDGE graphs ,INFORMATION overload ,RECOMMENDER systems ,PREDICTION models ,USER experience ,ELECTRONIC newspapers - Abstract
Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
31. 个性化新闻推荐方法研究综述.
- Author
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孟祥福, 霍红锦, 张霄雁, 王琬淳, and 朱金侠
- Abstract
Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
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32. Self-supervised robust Graph Neural Networks against noisy graphs and noisy labels.
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Yuan, Jinliang, Yu, Hualei, Cao, Meng, Song, Jianqing, Xie, Junyuan, and Wang, Chongjun
- Subjects
GRAPH labelings ,LEARNING modules - Abstract
In the paper, we first explore a novel problem of training the robust Graph Neural Networks (GNNs) against noisy graphs and noisy labels. To the problem, we propose a general Self-supervised Robust Graph Neural Network framework that consists of three modules: graph structure learning, sample selection, and self-supervised learning. Specifically, we first employ a graph structure learning approach to obtain an optimal graph structure. Next, using this structure, we use a clustering algorithm to generate pseudo-labels that represent the clusters. We then design a sample selection strategy based on these pseudo-labels to select nodes with clean labels. Additionally, we introduce a self-supervised learning technique where low-level layer parameters are shared with GNNs to predict pseudo-labels. We jointly train the graph structure learning module, the GNNs model, and the self-supervised model. Finally, we conduct extensive experiments on four real-world datasets, demonstrating the superiority of our methods compared with state-of-the-art methods for semi-supervised node classification under noisy graphs and noisy labels. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Uni-directional graph structure learning-based multivariate time series anomaly detection with dynamic prior knowledge
- Author
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He, Shiming, Li, Genxin, Wang, Jin, Xie, Kun, and Sharma, Pradip Kumar
- Published
- 2024
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34. A self-attention dynamic graph convolution network model for traffic flow prediction
- Author
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Liao, Kaili, Zhou, Wuneng, and Wu, Wanpeng
- Published
- 2024
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35. SGK-Net: A Novel Navigation Scene Graph Generation Network
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Wenbin Yang, Hao Qiu, Xiangfeng Luo, and Shaorong Xie
- Subjects
navigation scene graph generation ,multimodal fusion ,graph structure learning ,message passing ,Chemical technology ,TP1-1185 - Abstract
Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation.
- Published
- 2024
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36. Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation
- Author
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Dongdong An, Zongxu Pan, Qin Zhao, Wenyan Liu, and Jing Liu
- Subjects
graph neural networks ,unsupervised learning ,graph structure learning ,contrastive learning on graphs ,Mathematics ,QA1-939 - Abstract
Graph neural networks (GNNs) are effective for structured data analysis but face reduced learning accuracy due to noisy connections and the necessity for explicit graph structures and labels. This requirement constrains their usability in diverse graph-based applications. In order to address these issues, considerable research has been directed toward graph structure learning that aims to denoise graph structures concurrently and refine GNN parameters. However, existing graph structure learning approaches encounter several challenges, including dependence on label information, underperformance of learning algorithms, insufficient data augmentation methods, and limitations in performing downstream tasks. We propose Uogtag, an unsupervised graph structure learning framework to address these challenges. Uogtag optimizes graph topology through the selection of suitable graph learners for the input data and incorporates contrastive learning with adaptive data augmentation, enhancing the learning and applicability of graph structures for downstream tasks. Comprehensive experiments on various real-world datasets demonstrate Uogtag’s efficacy in managing noisy graphs and label scarcity.
- Published
- 2024
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- View/download PDF
37. Long-tailed graph neural networks via graph structure learning for node classification.
- Author
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Lin, Junchao, Wan, Yuan, Xu, Jingwen, and Qi, Xingchen
- Subjects
ACQUISITION of data ,CLASSIFICATION - Abstract
Long-tailed methods have gained increasing attention and achieved excellent performance due to the long-tailed distribution in graphs, i.e., many small-degree tail nodes have limited structural connectivity. However, real-world graphs are inevitably noisy or incomplete due to error-prone data acquisition or perturbations, which may violate the assumption that the raw graph structure is ideal for long-tailed methods. To address this issue, we study the impact of graph perturbation on the performance of long-tailed methods, and propose a novel GNN-based framework called LTSL-GNN for graph structure learning and tail node embedding enhancement. LTSL-GNN iteratively learns the graph structure and tail node embedding enhancement parameters, allowing information-rich head nodes to optimize the graph structure through multi-metric learning and further enhancing the embeddings of the tail nodes with the learned graph structure. Experimental results on six real-world datasets demonstrate that LTSL-GNN outperforms other state-of-the-art baselines, especially when the graph structure is disturbed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Patient multi-relational graph structure learning for diabetes clinical assistant diagnosis
- Author
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Yong Li and Li Feng
- Subjects
patient multi-relational graph ,graph neural networks ,graph structure learning ,diabetes assistant diagnosis ,Biotechnology ,TP248.13-248.65 ,Mathematics ,QA1-939 - Abstract
The rapid accumulation of electronic health records (EHRs) and the advancements in data analysis technology have laid the foundation for research and clinical decision-making in the healthcare community. Graph neural networks (GNNs), a deep learning model family for graph embedding representations, have been widely used in the field of smart healthcare. However, traditional GNNs rely on the basic assumption that the graph structure extracted from the complex interactions among the EHRs must be a real topology. Noisy connections or false topology in the graph structure leads to inefficient disease prediction. We devise a new model named PM-GSL to improve diabetes clinical assistant diagnosis based on patient multi-relational graph structure learning. Specifically, we first build a patient multi-relational graph based on patient demographics, diagnostic information, laboratory tests, and complex interactions between medicines in EHRs. Second, to fully consider the heterogeneity of the patient multi-relational graph, we consider the node characteristics and the higher-order semantics of nodes. Thus, three candidate graphs are generated in the PM-GSL model: original subgraph, overall feature graph, and higher-order semantic graph. Finally, we fuse the three candidate graphs into a new heterogeneous graph and jointly optimize the graph structure with GNNs in the disease prediction task. The experimental results indicate that PM-GSL outperforms other state-of-the-art models in diabetes clinical assistant diagnosis tasks.
- Published
- 2023
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- View/download PDF
39. A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction †.
- Author
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Li, Bin, Fan, Yunlong, Gao, Miao, Sataer, Yikemaiti, and Gao, Zhiqiang
- Subjects
PARSING (Computer grammar) ,REPRESENTATIONS of graphs ,RUNNING speed ,LEARNING ability ,ENGLISH language ,CHINESE language - Abstract
Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs' powerful ability in learning expressive graph representations. However, most of these works learn graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone, and the errors introduced in the static graph cannot be corrected and might accumulate in later stages, and (2) the graph construction stage and graph representation learning stage are disjoined, which negatively affects the model's running speed. In this paper, we propose a joint-learning-based dynamic graph learning framework and apply it to two typical structured prediction tasks: syntactic dependency parsing, which aims to predict a labeled tree, and semantic dependency parsing, which aims to predict a labeled graph, for jointly learning the graph structure and graph representations. Experiments are conducted on four datasets: the Universal Dependencies 2.2, the Chinese Treebank 5.1, the English Penn Treebank 3.0 in 13 languages for syntactic dependency parsing, and the SemEval-2015 Task 18 dataset in three languages for semantic dependency parsing. The experimental results show that our best-performing model achieves a new state-of-the-art performance on most language sets of syntactic dependency and semantic dependency parsing. In addition, our model also has an advantage in running speed over the static graph-based learning model. The outstanding performance demonstrates the effectiveness of the proposed framework in structured prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Adaptive Graph-Learning Convolutional Network for Multi-Node Offshore Wind Speed Forecasting.
- Author
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Liu, Jingjing, Yang, Xinli, Zhang, Denghui, Xu, Ping, Li, Zhuolin, and Hu, Fengjun
- Subjects
WIND forecasting ,WIND speed ,WIND power - Abstract
Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability in modeling dependencies. However, existing methods usually require a pre-defined graph structure, which is not optimal for the downstream task and limits the application scope of GNN. In this paper, we propose adaptive graph-learning convolutional networks (AGLCN) that can automatically infer hidden associations among multi-nodes through a graph-learning module. It simultaneously integrates the temporal and graph convolutional modules to capture temporal and spatial features in the data. Experiments are conducted on real-world multi-node wind speed data from the China Sea. The results show that our model achieves state-of-the-art results in all multi-scale wind speed predictions. Moreover, the learned graph can reveal spatial correlations from a data-driven perspective. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. Time-lagged relation graph neural network for multivariate time series forecasting.
- Author
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Feng, Xing, Li, Hongru, and Yang, Yinghua
- Subjects
- *
GRAPH neural networks , *TIME series analysis , *LEARNING modules , *FORECASTING - Abstract
Recently, Graph Neural Network-based approaches (GNNs) have been widely studied in Multivariate Time Series (MTS) prediction, which could extract information from the closely related variables for prediction. The variables contained in MTS data are lagged correlated, and the future trends of the lagging variables are guided by the leading variables. However, as the existing approaches only focus on delay-free relations, they cannot utilize the guidance information in leading variables to achieve accurate prediction. To address this issue, we propose a novel frame called the Time-Lagged Relation Graph Neural Network (TLGNN) including two key components: the time-lagged relation graph and the time-lagged relation graph learning. The time-lagged relation graph could explicitly model the time-delay relations among MTS variables by connecting variable nodes at lag intervals. The graph learning module could adaptively extract the time-delay relations among MTS variables. Based on the novel designed graph structure, the TLGNN could extract the guidance information from previous values of leading variables to generate more efficient feature representations for prediction. In experiments, the prediction accuracy is significantly improved due to the full exploration of the time-delay relations. Compared with existing methods, the TLGNN achieves the best results in both the single-step prediction and the multi-step prediction tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2025
- Full Text
- View/download PDF
42. Unified node, edge and motif learning networks for graphs.
- Author
-
Ho Thi Thanh, Tuyen and Le, Bac
- Subjects
- *
GRAPH neural networks , *INFORMATION resources , *DEEP learning , *MUTAGENESIS , *TOXICOLOGY , *ALGORITHMS - Abstract
Driven by the success of deep learning in recent years, many Graph Neural Networks have been proposed to address various tasks in graph learning. However, most of them are suffering from limitations on detecting and representing subgraph structures/information. First, most motif-based methods are relying on a predefined motif vocabulary or human designed algorithms to find motifs, suggesting they have not leveraged the power of deep networks to automatically learn underlying topological structures. Second, they mainly concentrate on node information and ignore the important roles of edge features and local structures. This paper presents a novel neural network framework (Node, Edge and MOtif Learning Network - NemoNet), integrating node and edge features in addition to the capacity of motif discovery and graph structure learning, eliminating these limitations. To address the diverse changes in subgraph topology, we convert graphs into rich structure-driven representation tensors before training the network. NemoNet is parameterized by motif filters that are initialized and updated during training. It is also designed to accept node and edge features as input and learn high-level embeddings from all information sources (node, edge and motif). Experiments on molecular datasets show NemoNet can learn graph structures, leverage these information sources effectively and achieve the highest accuracy (88.89% and 60.75%) in MUTAG (MUTAGenesis dataset) and PTC (Predictive Toxicology Challenge dataset), and a competitive performance against existing Graph Neural Networks in the other datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Physics-informed gated recurrent graph attention unit network for anomaly detection in industrial cyber-physical systems.
- Author
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Wu, Weiqiang, Song, Chunyue, Zhao, Jun, and Xu, Zuhua
- Subjects
- *
ANOMALY detection (Computer security) , *CYBER physical systems , *INDUSTRIALISM , *BINOMIAL distribution , *INFRASTRUCTURE (Economics) , *CYBERBULLYING - Abstract
Industrial cyber-physical systems (ICPSs) play an important role in many critical infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a novel end-to-end physics-informed gated recurrent graph attention unit network (PGRGAT) for unsupervised anomaly detection. Different from existing data-driven methods, PGRGAT combines prior knowledge with process data to improve the modeling performance and interpretability. Firstly, a physics-informed graph structure learning module is designed to explicitly model the dependencies among variables into a directed graph. This module learns the Bernoulli distribution on graph edges and generates a discrete graph using Gumbel-softmax sampling. Moreover, prior knowledge is introduced as graph regularization which constrains the graph to adhere to the underlying physics. Then, based on the learned graph structure, a novel gated recurrent graph attention unit network is proposed to simultaneously encode the inter-variable structural dependencies and intra-variable temporal dependencies for anomaly detection. By this means, the information of irrelevant variables can be discarded to improve the sensitivity to anomalies. Finally, the effectiveness of the proposed method is verified through two real-world industrial cases. • A novel end-to-end PGRGAT is proposed for unsupervised anomaly detection in ICPSs. • The dependencies among variables are modeled into a directed graph by a PGSL. • Graph regularization constrains the graph to adhere to the underlying physics. • The inter-variable and intra-variable dependencies are encoded by a GRGAU network. • We use prior knowledge to improve the modeling performance and interpretability. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Generic Structure Extraction with Bi-Level Optimization for Graph Structure Learning.
- Author
-
Yin, Nan and Luo, Zhigang
- Subjects
- *
BILEVEL programming , *GLOBAL method of teaching - Abstract
Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph structure, improve the robustness of GNN merely from a local view by considering the local information related to each edge and indiscriminately applying the mechanism across edges, which may suffer from the local structure heterogeneity of the graph (i.e., the uneven distribution of inter-class connections over nodes). To overcome the drawbacks, we extract the graph structure as a learnable parameter and jointly learn the structure and common parameters of GNN from the global view. Excitingly, the common parameters contain the global information for nodes features mapping, which is also crucial for structure optimization (i.e., optimizing the structure relies on global mapping information). Mathematically, we apply a generic structure extractor to abstract the graph structure and transform GNNs in the form of learning structure and common parameters. Then, we model the learning process as a novel bi-level optimization, i.e., Generic Structure Extraction with Bi-level Optimization for Graph Structure Learning (GSEBO), which optimizes GNN parameters in the upper level to obtain the global mapping information and graph structure is optimized in the lower level with the global information learned from the upper level. We instantiate the proposed GSEBO on classical GNNs and compare it with the state-of-the-art GSL methods. Extensive experiments validate the effectiveness of the proposed GSEBO on four real-world datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms.
- Author
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Zhang, Zheng, Ao, Xiang, Tessone, Claudio J., Liu, Gang, Zhou, Mingyang, Mao, Rui, and Liao, Hao
- Subjects
- *
GRAPH neural networks , *REINFORCEMENT learning , *INTERNET fraud , *REPRESENTATIONS of graphs , *FRAUD - Abstract
Fraudulent activities on e-commerce platforms, such as spamming product reviews or fake payment behaviors, seriously mislead users' purchasing decisions and harm platform integrity. To effectively identify fraudsters, recent research mainly attempts to employ graph neural networks (GNNs) with aggregating neighborhood features for detecting the fraud suspiciousness. However, GNNs are vulnerable to carefully-crafted perturbations in the graph structure, and the camouflage strategies of collusive fraudsters limit the effectiveness of GNNs-based fraud detectors. To address these issues, a novel multiplex graph fusion network with reinforcement structure learning (RestMGFN) is proposed in this paper to reveal the collaborative camouflage review fraud. Specifically, an adaptive graph structure learning module is designed to generate high-quality graph representation by utilizing paradigm constraints on the intrinsic properties of graph. Multiple relation-specific graphs are then constructed using meta-path search for capturing the deep semantic features of fraudulent activities. Finally, we incorporate the multiplex graph representations module into a unified framework, jointly optimizing the graph structure and corresponding embedding representations. Comprehensive experiments on real-world datasets verify the effectiveness and robustness of the proposed model compared with state-of-the-art approaches. • A novel multiplex graph fusion network with reinforcement structure learning. • We well capture the structural relationship and semantic feature. • RestMGFN model can eliminate the perturbation of camouflage fraud. • We achieve high detection performance in face of camouflaged fraud. [ABSTRACT FROM AUTHOR]
- Published
- 2025
- Full Text
- View/download PDF
46. Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model.
- Author
-
Wang, Tingting, Li, Zhuolin, Geng, Xiulin, Jin, Baogang, and Xu, Lingyu
- Subjects
OCEAN temperature ,TIME series analysis ,ARTIFICIAL neural networks ,FORECASTING ,LEARNING modules ,CHARTS, diagrams, etc. - Abstract
The accurate prediction of sea surface temperature (SST) is the basis for our understanding of local and global climate characteristics. At present, the existing sea temperature prediction methods fail to take full advantage of the potential spatial dependence between variables. Among them, graph neural networks (GNNs) modeled on the relationships between variables can better deal with space–time dependency issues. However, most of the current graph neural networks are applied to data that already have a good graph structure, while in SST data, the dependency relationship between spatial points needs to be excavated rather than existing as prior knowledge. In order to predict SST more accurately and break through the bottleneck of existing SST prediction methods, we urgently need to develop an adaptive SST prediction method that is independent of predefined graph structures and can take full advantage of the real temporal and spatial correlations hidden indata sets. Therefore, this paper presents a graph neural network model designed specifically for space–time sequence prediction that can automatically learn the relationships between variables and model them. The model automatically extracts the dependencies between sea temperature multi-variates by embedding the nodes of the adaptive graph learning module, so that the fine-grained spatial correlations hidden in the sequence data can be accurately captured. Figure learning modules, graph convolution modules, and time convolution modules are integrated into a unified end-to-end framework for learning. Experiments were carried out on the Bohai Sea surface temperature data set and the South China Sea surface temperature data set, and the results show that the model presented in this paper is significantly better than other sea temperature model predictions in two remote-sensing sea temperature data sets and the surface temperature of the South China Sea is easier to predict than the surface temperature of the Bohai Sea. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting
- Author
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Xu, Nancy, Kosma, Chrysoula, Vazirgiannis, Michalis, Xu, Nancy, Kosma, Chrysoula, and Vazirgiannis, Michalis
- Abstract
Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance., QC 20240409Part of ISBN 9783031534676
- Published
- 2024
- Full Text
- View/download PDF
48. Adaptive Graph-Learning Convolutional Network for Multi-Node Offshore Wind Speed Forecasting
- Author
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Jingjing Liu, Xinli Yang, Denghui Zhang, Ping Xu, Zhuolin Li, and Fengjun Hu
- Subjects
multi-node wind speed forecasting ,unknown complex dependencies ,graph neural networks ,pre-defined graph ,graph structure learning ,Naval architecture. Shipbuilding. Marine engineering ,VM1-989 ,Oceanography ,GC1-1581 - Abstract
Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability in modeling dependencies. However, existing methods usually require a pre-defined graph structure, which is not optimal for the downstream task and limits the application scope of GNN. In this paper, we propose adaptive graph-learning convolutional networks (AGLCN) that can automatically infer hidden associations among multi-nodes through a graph-learning module. It simultaneously integrates the temporal and graph convolutional modules to capture temporal and spatial features in the data. Experiments are conducted on real-world multi-node wind speed data from the China Sea. The results show that our model achieves state-of-the-art results in all multi-scale wind speed predictions. Moreover, the learned graph can reveal spatial correlations from a data-driven perspective.
- Published
- 2023
- Full Text
- View/download PDF
49. Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction.
- Author
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Li, Ming, Zhuang, Xiaosheng, Bai, Lu, and Ding, Weiping
- Subjects
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STUDENT engagement , *MULTIMODAL user interfaces , *SMART structures , *SUPERVISED learning , *MULTISENSOR data fusion , *EDUCATIONAL outcomes , *LEARNING - Abstract
With the increasing availability of multimodal educational data, there is a growing need to effectively integrate and exploit multiple data sources to enhance student engagement prediction accuracy. In this work, we propose a framework that combines multimodal data, including visual, textual and acoustic modalities that reflect the students' personalities, their demographic information, their learning behavior and attention, with graph learning techniques. Specifically, 3D Haar semi-tight framelet transforms are developed to capture the inter-modal relationships and model the complex interactions within the multimodal data. Subsequently, we introduce a novel module for adaptive graph structure learning based on the spectrum of multimodal data, which takes into consideration the distinct contributions of low-pass and high-pass framelet coefficients by adaptively weighing their impact. By addressing a standard semi-supervised node classification problem, we successfully achieve the objective of student engagement prediction. The experiment evaluations on a real-world educational dataset demonstrate the effectiveness of the proposed approach, achieving superior performance compared to state-of-the-art methods. Our experimental studies demonstrate the importance of multimodal graph learning in accurately predicting student engagement and its potential to enhance educational outcomes. • A novel multimodal graph learning framework for student engagement prediction is proposed. • 3D Haar semi-tight framelet transform is introduced to facilitate multimodal data fusion. • Spectrum-based graph structure learning is proposed to build multimodal graph. • Our framework outperforms several baselines on a real-world multimodal educational dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Spatio-temporal Fourier enhanced heterogeneous graph learning for traffic forecasting.
- Author
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Zhang, Wenchang, Wang, Hua, and Zhang, Fan
- Subjects
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TRAFFIC estimation , *COMPUTER network traffic , *TRAFFIC flow , *LEARNING modules , *PREDICTION models - Abstract
Traffic flow prediction is of paramount importance in the field of spatio-temporal forecasting. In recent years, research efforts have primarily been directed towards developing intricate graph convolutional networks (GCNs) to capture spatial complexities. However, this has inadvertently led to the neglect of the inherent temporal correlations in traffic prediction, as well as the heterogeneity of graph structures. As a result, existing models show limited efficacy when dealing with the complex nature of traffic data. To address this issue, this paper introduces a novel traffic prediction model: the Fourier-enhanced heterogeneous graph convolution attention recurrent network (FEHGCARN). This model integrates historical information and incorporates a graph convolution attention recurrent unit (GCARU), meticulously engineered to effectively capture spatio-temporal dependencies. Additionally, it features a Fourier-enhanced heterogeneous graph learning module, which facilitates the acquisition of complex relationships among nodes in the frequency domain. Notably, this memory network excels at recognizing abrupt traffic conditions. To validate our approach, we conducted comprehensive comparisons using three authentic datasets and benchmarked our model against six state-of-the-art baseline methods. The experimental results unequivocally demonstrate the superior performance of our model across all evaluation metrics. • Fourier-enhanced heterogeneous graph convolution attention recurrent network. • Capturing spatio-temporal dependencies in graph convolution attention units. • The novel approach is the Fourier-enhanced heterogeneous graph learner. • Our model surpasses baselines, achieving state-of-the-art results in experiments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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