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   Articles with "adaptive graph" as a keyword



AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction

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recommendations! Published in 2025 at "ACS Omega"

DOI: 10.1021/acsomega.5c04006

Abstract: Identifying side effects is crucial for drug development and postmarket surveillance. Several computational methods based on graph neural networks (GNNs) have been developed, leveraging the topological structure and node attributes in graphs with promising results.… read more here.

Keywords: adaptive graph; drug side; drug; agrl dse ... See more keywords

Batch process soft sensing based on data-stacking multiscale adaptive graph neural network

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recommendations! Published in 2024 at "Measurement Science and Technology"

DOI: 10.1088/1361-6501/ad8be6

Abstract: Soft sensing technology has found extensive application in predicting key quality variables in batch processes. However, its application in batch process is limited by the uneven batch length, the correlation of data and the difficulty… read more here.

Keywords: adaptive graph; process; graph neural; network ... See more keywords

spaCI: deciphering spatial cellular communications through adaptive graph model

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recommendations! Published in 2022 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbac563

Abstract: Abstract Cell–cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for… read more here.

Keywords: cell; cellular communications; spaci; graph model ... See more keywords

Adaptive Graph Representation for Clustering

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3221150

Abstract: Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in… read more here.

Keywords: graph construction; graph; representation graph; representation ... See more keywords

$\mathrm{E}^{2}\text{AG}$: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling

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recommendations! Published in 2025 at "IEEE/CAA Journal of Automatica Sinica"

DOI: 10.1109/jas.2024.124884

Abstract: Adaptive graph neural networks (AGNNs) have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables. This article introduces a novel GNN framework, termed entropy-regularized ensemble… read more here.

Keywords: adaptive graph; regularized ensemble; mathbf; entropy regularized ... See more keywords

A-Gamba: An Adaptive Graph-Mamba Model for Traffic Prediction in Wireless Cellular Networks

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recommendations! Published in 2025 at "IEEE Wireless Communications Letters"

DOI: 10.1109/lwc.2025.3557313

Abstract: Accurate traffic prediction is essential for optimizing network resource allocation in wireless cellular networks. In this letter, we propose an adaptive graph-Mamba (A-Gamba) model for traffic prediction. The proposed model utilizes a bidirectional-Mamba block and… read more here.

Keywords: traffic prediction; adaptive graph; graph; model ... See more keywords

Hyperspectral Image Classification Based on Superpixel Feature Subdivision and Adaptive Graph Structure

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recommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2022.3153446

Abstract: The graph-based hyperspectral image classification (HSIC) method has attracted wide attention because it can extract information with a non-Euclidean structure. Many graph-based HSIC works have achieved good results, but unresolved technical issues remain. For example,… read more here.

Keywords: structure; graph structure; classification; graph ... See more keywords

SAGT: Structure-Adaptive Graph Transformer for Hyperspectral Image Classification

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recommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2025.3634114

Abstract: Hyperspectral images (HSIs) are vital for scene analysis, as they capture detailed spatial and spectral information to characterize surface materials. However, accurate HSI classification is challenged by significant intraclass spectral variability and spatial complexity. To… read more here.

Keywords: classification; structure adaptive; adaptive graph; sagt ... See more keywords

AGP-Net: Adaptive Graph Prior Network for Image Denoising

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recommendations! Published in 2024 at "IEEE Transactions on Industrial Informatics"

DOI: 10.1109/tii.2023.3316184

Abstract: Image denoising is a critical problem in industrial information applications since noisy images can have adverse effects on the performance of many industrial tasks. Currently, Transformer structures and graph convolutional networks (GCNs) have been widely… read more here.

Keywords: adaptive graph; image; agp net; image denoising ... See more keywords

Spatio-Temporal Contrastive Learning-Based Adaptive Graph Augmentation for Traffic Flow Prediction

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recommendations! Published in 2025 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2024.3487982

Abstract: Traffic flow prediction plays a pivotal role in intelligent transportation systems. While previous efforts have made significant advances in modeling spatio-temporal dependencies, the traffic data collected by sensors in real-world scenarios is inherently limited, and… read more here.

Keywords: adaptive graph; traffic flow; spatio temporal; traffic ... See more keywords

Stacked Network to Realize Spectral Clustering With Adaptive Graph Learning

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2023.3327043

Abstract: Spectral clustering with graph learning usually performs eigen-decomposition on the adaptive graph to obtain embedded representation for clustering. In terms of adaptive graph learning, the embedded representation is usually treated as the principal component of… read more here.

Keywords: adaptive graph; graph learning; embedded representation; network ... See more keywords