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   Articles with "graph transformer" as a keyword



Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal-Organic Frameworks

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recommendations! Published in 2022 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.2c00876

Abstract: Predicting interactions between metal-organic frameworks (MOFs) and their adsorbates based on structures is critical to design high-performance porous materials. Many gas uptake prediction models have been proposed, but adsorption isotherm prediction is still challenging for… read more here.

Keywords: adsorption; adsorption isotherms; network; organic frameworks ... See more keywords

Fluid identification with Graph Transformer using well logging data

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recommendations! Published in 2024 at "Physics of Fluids"

DOI: 10.1063/5.0211182

Abstract: The prediction of fluid through well logging is a cornerstone in guiding exploratory efforts in the energy sector. Comprehending the fluid composition beneath the surface empowers exploration teams to effectively gauge the extent, reserves, and… read more here.

Keywords: logging data; fluid identification; graph transformer; well logging ... See more keywords

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbae425

Abstract: Abstract Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL… read more here.

Keywords: synthetic lethality; mlec isl; prediction; connectivity ... See more keywords

GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge

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recommendations! Published in 2025 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbaf584

Abstract: Abstract The inference of gene regulatory networks (GRNs) is critical for understanding the regulatory mechanisms underlying cellular development, functional specialization, and disease progression. Predicting regulatory gene interactions—often framed as a link prediction task—is a foundational… read more here.

Keywords: inference; network; graph transformer; gene ... See more keywords
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Integrating Heterogeneous Graphs Using Graph Transformer Encoder for Solving Math Word Problems

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recommendations! Published in 2023 at "IEEE Access"

DOI: 10.1109/access.2023.3257571

Abstract: This paper introduces a novel method that integrates structural information with training deep neural models to solve math word problems. Prior works adopt the graph structure to represent rich information residing in the input sentences.… read more here.

Keywords: heterogeneous graphs; graph; transformer encoder; math word ... See more keywords

mBGT: Encoding Brain Signals With Multimodal Brain Graph Transformer

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recommendations! Published in 2025 at "IEEE Transactions on Consumer Electronics"

DOI: 10.1109/tce.2024.3425478

Abstract: Leveraging the multimodal brain signals collected from various electronic devices, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data, has been regarded as a promising technique for automated brain disease diagnosis. Existing studies on… read more here.

Keywords: brain; brain graph; brain signals; graph transformer ... See more keywords

Uncertainty-Aware Superpoint Graph Transformer for Weakly Supervised 3-D Semantic Segmentation

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recommendations! Published in 2025 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2025.3543036

Abstract: Weakly supervised 3-D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3-D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still… read more here.

Keywords: superpoint graph; uncertainty aware; uncertainty; graph transformer ... See more keywords

Local Information-Enhanced Graph-Transformer for Hyperspectral Image Change Detection With Limited Training Samples

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recommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2023.3269892

Abstract: Hyperspectral image change detection (HSI-CD) is a challenging task that focuses on identifying the differences between multitemporal HSIs. The recent advancement of convolutional neural network (CNN) has made great progress on HSIs-CD. However, due to… read more here.

Keywords: information; local information; image change; graph transformer ... See more keywords

Local Feature Augmented Graph Transformer for Enhanced Industrial Process Fault Diagnosis

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recommendations! Published in 2025 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2025.3612657

Abstract: Industrial fault diagnosis is a core technical support for ensuring the safe and efficient operation of modern industrial measurement systems. However, the key challenges of complex spatiotemporal correlations and sparse local fault features in sensor… read more here.

Keywords: local feature; fault diagnosis; graph transformer; feature ... See more keywords

Handling Low Homophily in Recommender Systems With Partitioned Graph Transformer

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recommendations! Published in 2025 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3485880

Abstract: Modern recommender systems derive predictions from an interaction graph that links users and items. To this end, many of today's state-of-the-art systems use graph neural networks (GNNs) to learn effective representations of these graphs under… read more here.

Keywords: homophily recommender; partitioned graph; recommender systems; graph transformer ... See more keywords

TGformer: A Graph Transformer Framework for Knowledge Graph Embedding

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recommendations! Published in 2025 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3486747

Abstract: Knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches learn the embedding of missing entities by a single triple only.… read more here.

Keywords: knowledge graph; graph; graph embedding; graph transformer ... See more keywords