graph neural networks

A Dual-Layer Aggregation Graph Neural Network for Rapid Indoor Pollutant Dispersion Prediction

Validation across multiple building configurations demonstrates that the proposed model effectively captures physical characteristics of pollutant dispersion in distant rooms and accurately predicts toxic gas dispersion under different layouts, while delivering computational speeds approximately one order of magnitude...

Synergizing large and small models in cloud-edge continuum: a spatiotemporal hypergraph approach for dynamic offloading

This paper proposes a spatiotemporal hypergraph-driven framework integrating high-order topological feature extraction with dynamic resource modeling, and introduces dynamic hypergraph sequences to naturally encompass local conflict domains, mitigating the topological blind spots and “over-smoothing” issues inherent in...

Geautomatiseerde objectmanipulatie in hardfruitboomgaarden – Robotisch snoeien en oogsten

Growing pome fruit requires knowledge of the tree and its circumstances. Throughout the year, experienced cultivators manage the orchard in various ways, including certain manipulations in the tree such as pruning, thinning and harvesting. Nowadays, these tasks are still mainly performed manually. An extensive activity...

Thermodynamically-informed graph neural network for spatiotemporal temperature field reconstruction in heat transfer systems

Retrieval-Augmented Residual Graph Neural Network for Protein-Protein Interaction Site Prediction.

RGLLA-PPIS, a novel multimodal prediction model that integrates retrieval-augmented learning and residual GNNs for PPIS identification, outperforms several state-of-the-art baselines in both accuracy and robustness and demonstrates its potential to guide real-world protein engineering tasks.

IGNN: An Inference Graph Neural Network for Data Sensitivity Assessment Considering Data Inference Leakage in Cyber-Physical Power Grid

Modern Cyber-Physical Power Grids (CPPG) rely heavily on fine-grained data exchange, yet they face the concealed threat of Data Inference Leakage (DIL), where attackers infer sensitive parameters from seemingly non-sensitive data. Existing risk assessment methods typically focus on direct cyber-attacks or employ static...

Montage-guided fusion network for long-term scalp electroencephalography seizure detection

MGF-Net, a montage-guided fusion network for seizure detection from long-term scalp EEG, is proposed and results demonstrate MGF-Net's effectiveness for seizure detection from long-term scalp EEG.

Data-free neural mesh inpainting: preserving topology and geometry

This paper introduces a data-free neural mesh inpainting framework that operates directly on incomplete input meshes while preserving the original mesh connectivity in a self-supervised graph convolutional network.

3D Dynamic wrinkling and deformation inference model of knitted garments integrating Graph Neural networks and node topological constraints

Deep Learning-Based GNN-Attention Framework for Near-Field Channel Modeling and Beamforming in Sixth-Generation Wireless Systems: A Simulation Study.

The results reveal that integrating near-field channel modeling with GNN-based attention learning can serve as an efficient and accurate beamforming technique for future 6G wireless networks that utilize XL-IMS technology.

LesionGraph-Net: A clinically motivated Graph Neural Network for accurate skin cancer diagnosis using lesion-focused features

Semantic-Aware Subgraph State Space Model for WSI Classification in Histopathology

The Semantic-Aware Subgraph State Space Model (SASG-SSM), a flexible and efficient framework for whole slide image (WSI) classification, is proposed, which integrates local structural information within semantic units with global contextual information arising from their distribution and co-occurrence across the WSI, w...