graph neural networks

Joint dynamic pruning method for graph neural networks under traffic prediction scenarios

Graph Theory, Graph Neural Networks, and Reinforcement Learning in marine ecology: A systematic review of methodologies, theoretical rigour, and future directions

Orbital-geometric atomic line graph neural network-VoltaDelta framework for assisted screening of zinc-ion battery cathode materials

MF-HGNN: A multi-view fusion heterogeneous graph neural network for psychiatric disorder diagnosis

A dynamic spatio-temporal graph neural network adaptive to operating conditions: Learning evolutionary correlations in multi-sensor data for aero-engine

Topology-aware graph neural network for fusing sparse sensor data with finite element predictions in structural random vibration assessment

Simplicial Graph-Based Detection and Localization of Cyber Attacks Against Large-Scale Smart Grids

Ensuring the resilience of large-scale power systems against cyber attacks is critical to maintaining the stability of modern cyber-physical energy systems. Existing machine learning (ML)-based detection frameworks predominantly focus on pairwise node interactions (i.e., edges) and often overlook capturing higher-order...

HVAC subsystem-aligned explainable graph neural network mixture of experts (GNN-MoE) for multi-horizon work order prediction

Dynamic response analysis method of parametric structures based on modal reduction basis predicted by physics-informed graph neural networks

Full-Reference Point Cloud Quality Assessment Using Graph Neural Network-Based Regression

Edge-Girth as a Structural Edge Feature for Graph Neural Networks

On graph discrimination it is proved a matching limitation: on graphs where every edge sees the same number of shortest cycles of the same length, the descriptor becomes constant and any model built on it collapses back to the 1-WL bound.

Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing

Inspired by graph-based manifold learning, a manifold-aware encoding strategy for general coded computing (GCC) is proposed and experiments demonstrate that the proposed strategy consistently and significantly reduces the mean squared recovery error under straggling compared with standard GCC.