141 results on '"graph structure learning"'
Search Results
2. StAlK: Structural Alignment based Self Knowledge distillation for Medical Image Classification
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Sharma, Saurabh, Kumar, Atul, Monpara, Jenish, and Chandra, Joydeep
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- 2024
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Catalog
3. GCD: Graph contrastive denoising module for GNNs in EEG classification
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Liu, Guanting, Yan, Ying, Cai, Jun, Qi Wu, Edmond, Fang, Shencun, David Cheok, Adrian, and Song, Aiguo
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- 2025
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4. NeighborGeo: IP geolocation based on neighbors
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Wang, Xinye, Zhao, Dong, Liu, Xinran, Zhang, Zhaoxin, and Zhao, Tianzi
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- 2025
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5. A novel spatial–temporal graph convolution network based on temporal embedding graph structure learning for multivariate time series prediction
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Lei, Tianyang, Li, Jichao, Yang, Kewei, and Gong, Chang
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- 2025
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6. 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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7. 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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8. AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction.
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Sang, Lei, Li, Honghao, Zhang, Yiwen, Zhang, Yi, and Yang, Yun
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GRAPH neural networks , *ARTIFICIAL intelligence - Abstract
The article introduces the Adaptive Graph Interaction Network (AdaGIN) for Click-Through Rate (CTR) prediction, aiming to improve feature interaction modeling in recommender systems. Topics discussed include the challenges in feature combination and interaction, the importance of modeling feature interactions across different orders, and the integration of Graph Neural Networks (GNNs) to address these issues for better CTR prediction performance. more...
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- 2025
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9. 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] more...
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- 2024
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10. Proposal Semantic Relationship Graph Network for Temporal Action Detection.
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SU, SHAOWEN, ZHANG, YAN, and GAN, MINGGANG
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TIME-varying networks , *KNOWLEDGE transfer , *CLASSIFICATION , *FORECASTING , *VIDEOS - Abstract
Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during classification, often treating each proposal in isolation, which hampers accurate label prediction. In this article, we propose a novel method for inferring semantic relationships both within and between action proposals, guiding the fusion of action proposal features accordingly. Building on this approach, we introduce the Proposal Semantic Relationship Graph Network (PSRGN), an end-to-end model that leverages intra-proposal semantic relationship graphs to extract cross-scale temporal context and an inter-proposal semantic relationship graph to incorporate complementary neighboring information, significantly improving proposal feature quality and overall detection performance. This is the first method to apply graph structure learning in temporal action detection, adaptively constructing the inter-proposal semantic graph. Extensive experiments on two datasets demonstrate the effectiveness of our approach, achieving state-of-the-art (SOTA). Code and results are available at http://github.com/Riiick2011/PSRGN. [ABSTRACT FROM AUTHOR] more...
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- 2024
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11. scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data.
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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] more...
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- 2024
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12. Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network.
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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] more...
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- 2024
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13. Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network
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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. more...
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- 2024
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14. Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics
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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. more...
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- 2024
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15. Graph structure estimation neural network-based service classification
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Li, Yanxinwen, Xie, Ziming, Cao, Buqing, and Lou, Hua
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- 2024
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16. Enhanced multimodal recommendation systems through reviews integration: Enhanced multimodal recommendation...
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Fang, Hong, Liang, Jindong, and Sha, Leiyuxin
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- 2025
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17. Gmad: multivariate time series anomaly detection based on graph matching learning
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Kong, Jun, Wang, Kang, Jiang, Min, and Tao, Xuefeng
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- 2024
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18. 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
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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] more...
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- 2024
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19. SGK-Net: A Novel Navigation Scene Graph Generation Network.
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Yang, Wenbin, Qiu, Hao, Luo, Xiangfeng, and Xie, Shaorong
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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] more...
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- 2024
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20. MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting.
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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] more...
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- 2024
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21. Maximum a posteriori estimation in graphical models using local linear approximation.
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Sagar, Ksheera, Datta, Jyotishka, Banerjee, Sayantan, and Bhadra, Anindya
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INFERENTIAL statistics , *GRAPHICAL modeling (Statistics) , *EXPECTATION-maximization algorithms , *HORSESHOES - Abstract
Sparse structure learning in high‐dimensional Gaussian graphical models is an important problem in multivariate statistical inference, since the sparsity pattern naturally encodes the conditional independence relationship among variables. However, maximum a posteriori (MAP) estimation is challenging under hierarchical prior models, and traditional numerical optimization routines or expectation–maximization algorithms are difficult to implement. To this end, our contribution is a novel local linear approximation scheme that circumvents this issue using a very simple computational algorithm. Most importantly, the condition under which our algorithm is guaranteed to converge to the MAP estimate is explicitly stated and is shown to cover a broad class of completely monotone priors, including the graphical horseshoe. Further, the resulting MAP estimate is shown to be sparse and consistent in the ℓ2$$ {\ell}_2 $$‐norm. Numerical results validate the speed, scalability and statistical performance of the proposed method. [ABSTRACT FROM AUTHOR] more...
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- 2024
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22. MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction.
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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] more...
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- 2024
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23. Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution.
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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] more...
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- 2024
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24. Graph Transformer Network Incorporating Sparse Representation for Multivariate Time Series Anomaly Detection.
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Yang, Qian, Zhang, Jiaming, Zhang, Junjie, Sun, Cailing, Xie, Shanyi, Liu, Shangdong, and Ji, Yimu
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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] more...
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- 2024
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25. A Structure-Enhanced Heterogeneous Graph Representation Learning with Attention-Supplemented Embedding Fusion.
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Pham, Phu
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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] more...
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- 2024
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26. Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches
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Bloemheuvel, Stefan, van den Hoogen, Jurgen, and Atzmueller, Martin
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- 2024
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27. Ultra-Short-Term Power Prediction of Large Offshore Wind Farms Based on Spatiotemporal Adaptation of Wind Turbines.
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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] more...
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- 2024
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28. 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] more...
- Published
- 2024
- Full Text
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29. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.
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E, Zixuan, Qiao, Guanyu, Wang, Guohua, and Li, Yang
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DEEP learning , *DRUG discovery , *DRUG interactions , *MACHINE learning , *BASE pairs , *DRUG development - Abstract
• We propose an automated end-to-end graph structure learning model, GSL-DTI, for Drug-Target Interaction (DTI) prediction. In contrast to previous studies relying on manual rules, our approach incorporates an automatic graph structure learning method, which utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. • We conduct experiments on three public datasets and compare our method against competitive baselines. The experimental results demonstrate a significant outperformance of our model over state-of-the-art methods. • Furthermore, the introduction of graph structure learning offers a fresh perspective for DTI prediction research. To the best of our knowledge, GSL-DTI represents the first attempt to apply automatic graph structure learning to DTI tasks. It reduces the reliance on expert knowledge and yields improved node representations for downstream tasks. Motivation: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta -paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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30. Improving fraud detection via imbalanced graph structure learning.
- Author
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Ren, Lingfei, Hu, Ruimin, Liu, Yang, Li, Dengshi, Wu, Junhang, Zang, Yilong, and Hu, Wenyi
- Subjects
FRAUD investigation ,ITERATIVE learning control ,GRAPH algorithms ,COMPUTATIONAL complexity ,LEARNING modules ,STRUCTURAL frames - Abstract
Graph-based fraud detection methods have recently attracted much attention due to the rich relational information of graph-structured data, which may facilitate the detection of fraudsters. However, the GNN-based algorithms may exhibit unsatisfactory performance faced with graph heterophily as the fraudsters usually disguise themselves by deliberately making extensive connections to normal users. In addition to this, the class imbalance problem also causes GNNs to overfit normal users and perform poorly for fraudsters. To address these problems, we propose an Imbalanced Graph Structure Learning framework for fraud detection (IGSL for short). Specifically, nodes are picked with a devised multi-relational class-balanced sampler for mini-batch training. Then, an iterative graph structure learning module is proposed to iteratively construct a global homophilic adjacency matrix in the embedding domain. Further, an anchor node message passing mechanism is proposed to reduce the computational complexity of the constructing homophily adjacency matrix. Extensive experiments on benchmark datasets show that IGSL achieves significantly better performance even when the graph is heavily heterophilic and imbalanced. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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31. AMGCN: adaptive multigraph convolutional networks for traffic speed forecasting.
- Author
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Li, Chenghao, Zhao, Yahui, and Zhang, Zhenguo
- Subjects
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] more...
- Published
- 2024
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32. 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
- Subjects
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] more...
- Published
- 2024
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33. 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
- Subjects
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. more...
- Published
- 2024
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34. Multikernel Graph Structure Learning for Multispectral Point Cloud Classification
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Qingwang Wang, Zifeng Zhang, Jiangbo Huang, Tao Shen, and Yanfeng Gu
- Subjects
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. more...
- Published
- 2024
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35. 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. more...
- Published
- 2024
- Full Text
- View/download PDF
36. 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. more...
- Published
- 2023
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37. 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. more...
- Published
- 2023
- Full Text
- View/download PDF
38. 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] more...
- Published
- 2024
- Full Text
- View/download PDF
39. 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] more...
- Published
- 2024
- Full Text
- View/download PDF
40. A Survey of Personalized News Recommendation.
- Author
-
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] more...
- Published
- 2023
- Full Text
- View/download PDF
41. Self-supervised robust Graph Neural Networks against noisy graphs and noisy labels.
- Author
-
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] more...
- Published
- 2023
- Full Text
- View/download PDF
42. 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
- Full Text
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43. 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
- Full Text
- View/download PDF
44. SGK-Net: A Novel Navigation Scene Graph Generation Network
- Author
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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. more...
- Published
- 2024
- Full Text
- View/download PDF
45. 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. more...
- Published
- 2024
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46. Multi-view Contrastive Enhanced Heterogeneous Graph Structure Learning.
- Author
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Rui Bing, Guan Yuan, Fanrong Meng, Senzhang Wang, Shaojie Qiao, and Zhixiao Wang
- Subjects
REPRESENTATIONS of graphs ,ARTIFICIAL neural networks ,SEMANTIC networks (Information theory) - Abstract
As a learning method of heterogeneous graph representation, heterogeneous graph neural networks can effectively extract complex structural and semantic information from heterogeneous graphs, and perform excellently in node classification and link prediction tasks to provide strong support for the representation and analysis of knowledge graphs. Due to the existence of some noisy interactions or missing interactions in the heterogeneous graphs, the heterogeneous graph neural network incorporates erroneous neighbor features, thus affecting the overall performance of the model. To solve the above problems, in this paper we proposes a heterogeneous graph structure learning model enhanced by multi-view contrast. Firstly, the semantic information in the heterogeneous graph is maintained by the meta-path, and the similarity graph is generated by calculating the feature similarity among the nodes under each meta-path, which is fused with the meta-path graph to optimize the graph structure. By contrasting the similarity graph and meta-path graph as multiple views, the graph structure is optimized without supervision information, and the dependence on supervision signals is eliminated. Finally, for addressing the problem that the learning ability of the neural network model is insufficient at the initial training stage and there are often erroneous interactions in the generated graph structure, we design a progressive graph structure fusion method. Through incrementalweighted addition of meta-path graphs and similarity graphs, theweight of similarity graphs in the fusion is changed. This not only prevents erroneous interactions from being introduced in the initial training stage but also achieves the purpose of employing the interactions in similarity graphs to suppress interference interactions or complete missing interactions, which leads to the optimized heterogeneous structure. Meanwhile, node classification and node clustering are selected as the verification tasks of graph structure learning. The experimental results on four real heterogeneous graph datasets prove that the proposed learning method is feasible and effective. Compared with the optimal comparison model, the performance of this model has been significantly improved under both tasks. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
47. Structure-adaptive graph neural network with temporal representation and residual connections.
- Author
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Bi, Xin, Jiang, Qingling, Liu, Zhixun, Yao, Xin, Nie, Haojie, Yuan, George Y., Zhao, Xiangguo, and Sun, Yongjiao
- Subjects
- *
TIME-varying networks , *REPRESENTATIONS of graphs , *SMART structures , *LARGE-scale brain networks , *LEARNING ability - Abstract
Graph learning methods have boosted brain analysis for user healthcare, disease detection, and behavioral modeling. Spatially separated brain regions are functionally connected with different weights, enabling the classification of brain networks from the perspective of graph learning. However, existing methods based on graph neural networks mainly rely on the calculation of node feature correlation and manual threshold selection to obtain the graph structure, which disregards the temporal features of nodes and the latent information in the implicit graph structure. To address this problem, we propose a structure adaptive graph neural network with temporal representation and residual connections (TR-SAGNN) for brain network classification. First, we design a temporal attention learning module to learn the temporal features of the node itself. We design an end-to-end adaptive graph structure learning module based on the product-moment self-attention mechanism, which avoids manual threshold selection and obtains a more accurate graph structure. Second, we design a graph representation learning module based on a residual connection strategy to avoid the problem of insufficient propagation of node features. Last, we design a loss function to consider both the graph classification task and node classification task, which makes the model obtain better graph representation learning ability under the supervision of the node classification label. We conduct extensive experiments on the ANDI dataset. The results show that our model has better end-to-end adaptive graph construction capability as well as feature learning and classification performance. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
48. 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] more...
- Published
- 2023
- Full Text
- View/download PDF
49. Graph convolutional network with tree-guided anisotropic message passing.
- Author
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Wang, Ruixiang, Wang, Yuhu, Zhang, Chunxia, Xiang, Shiming, and Pan, Chunhong
- Subjects
- *
MESSAGE passing (Computer science) , *DEEP learning - Abstract
Graph Convolutional Networks (GCNs) with naive message passing mechanisms have limited performance due to the isotropic aggregation strategy. To remedy this drawback, some recent works focus on how to design anisotropic aggregation strategies with tricks on feature mapping or structure mining. However, these models still suffer from the low ability of expressiveness and long-range modeling for the needs of high performance in practice. To this end, this paper proposes a tree-guided anisotropic GCN, which applies an anisotropic aggregation strategy with competitive expressiveness and a large receptive field. Specifically, the anisotropic aggregation is decoupled into two stages. The first stage is to establish the path of the message passing on a tree-like hypergraph consisting of substructures. The second one is to aggregate the messages with constrained intensities by employing an effective gating mechanism. In addition, a novel anisotropic readout mechanism is constructed to generate representative and discriminative graph-level features for downstream tasks. Our model outperforms baseline methods and recent works on several synthetic benchmarks and datasets from different real-world tasks. In addition, extensive ablation studies and theoretical analyses indicate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
50. Graph structure learning layer and its graph convolution clustering application.
- Author
-
He, Xiaxia, Wang, Boyue, Li, Ruikun, Gao, Junbin, Hu, Yongli, Huo, Guangyu, and Yin, Baocai
- Subjects
- *
DEEP learning , *OPTIMIZATION algorithms , *REPRESENTATIONS of graphs , *SPARSE graphs , *MESSAGE passing (Computer science) , *MATHEMATICAL convolutions , *SELF-expression - Abstract
To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph. However, the data corrupted by noise may make the learned graph structure unreliable. In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by-layer with back-propagation. Specifically, we design a Graph Structure Learning layer before each Graph Convolutional layer to learn the sparse graph structure from the node representations, where the graph structure is implicitly determined by the solution to the optimal self-expression problem. This is one of the first works that uses an optimization process as a Graph Network layer, which is obviously different from the function operation in traditional deep learning layers. An efficient iterative optimization algorithm is given to solve the optimal self-expression problem in the Graph Structure Learning layer. Experimental results show that the proposed method can effectively defend the negative effects of inaccurate graph structures. The code is available at https://github.com/HeXiax/SSGNN. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
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