graph neural networks

When Vision Meets Graphs: A Survey on Graph Reasoning and Learning

This survey provides a first systematic overview of the emerging area of vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning, and organize existing work into three threads.

Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association

The results show that learned ISAC schedulers should be evaluated using constraint-aware feasibility metrics in addition to conventional accuracy measures, and show that feasibility alone does not guarantee robustness to false data injection attacks.

TRACE: Spatiotemporal Contact Memory Graph Network Simulator for Granular Dynamics

Learned graph simulators provide an efficient alternative to high-fidelity solvers for granular dynamics. However, granular motion depends strongly on inter-granular contact history, which is difficult to preserve when particle contacts form, break, and rearrange. Existing simulators mainly store temporal information i...

LevelSyn: Physical-Aware Logic Synthesis via Level-Asynchronous Graph Neural Networks

LevelSyn is a novel physical-aware logic synthesis framework that integrates hierarchical representation learning with a wirelength-driven optimization engine that significantly outperforms state-of-the-art methods on the EPFL benchmark suite.

Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts

Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Their application to spatial data is a natural extension, however its relatively unclear which message-passing operations, architectural configurations, and graph representation is best suited for classifying cha...

PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are highly heterogeneous in computing capacity, network bandwidth, and energy consumption, whic...

PrecipGNN: Physics-Informed Spatiotemporal Graph Neural Networks for High-Resolution Precipitation Nowcasting at Continental Scale

PrecipGNN: Physics-Informed Spatiotemporal Graph Neural Networks for High-Resolution Precipitation Nowcasting at Continental Scale

Neurostructural Correlates and Impulsive Mechanisms of Exercise Dependence in Middle-Aged and Elderly Adults: Evidence from Gray Matter and Structural Covariance Networks

Initial evidence of neurostructural markers underlying exercise dependence in the middle-aged and elderly adults are provided, demonstrating associations with regional GMV and whole-brain structural covariance networks.

GNMLC-Net: a graph neural inference and multi-layer context-aware fusion network for high-precision detection of avionics thermistor solder joints

A Cascaded Refinement and Context-Gated Semantic Injection mechanism is designed to enhance sensitivity to small targets by refining key feature information and improving the quality of feature representations, which effectively mitigates the semantic-spatial mismatch issue in deep networks while enhancing multi-scale...

Convergent pathology, divergent pathways: Structural network dissociation of trait and cognitive disinhibition in cocaine use disorder.

This study provides a dimensional framework for understanding the neurobiological organization of behavioral dysregulation in addiction by demonstrating that distinct dimensions of disinhibition in CUD are associated with partially overlapping but largely separable structural connectome architectures.

Vision transformer and graph neural networks based SuryaNet for yoga pose quality assessment

SuryaNet is introduced, a multi-task model with four technical contributions that establishes a new state-of-the-art in terms of quality assessment, while it also provides users with quality feedback.