graph neural networks

ST-KFNet-Based Framework for Online Metro Passenger OD Demand Forecasting under Uncertainty

A novel spatiotemporal Kalman filter network (ST-KFNet) framework for metro demand forecasting by integrating an autoregressive integrated moving average module, a Kalman filter (KF) module, and a convolutional neural network (CNN)-based variational autoencoder (VAE) module is proposed.

Enhancing agent-based wayfinding simulation for transportation hub evaluation with a visual memory graph Neural network (VM-GNN)

A graph neural network-based framework for hierarchical time series forecasting in retail

Multi-scale dynamic graph neural networks for response prediction and anomaly detection in ancient timber structures

Malope: Memory-Aware and Locality-Preserved Graph Neural Network Training

Training GNNs on large-scale graphs imposes significant memory constraints for storing substantial amounts of graph structures and node features. This often necessitates the use of memory extensions such as SSDs, leading to a memory hierarchy with disparities in capacity and access speed. Existing approaches focus on mitigating the read amplification of SSDs used as memory extensions to enhance overall performance. However, these methods fail to achieve optimal performance on heterogeneous memory architectures such as DRAM–NVM systems and overlook the efficient utilization of fast memory. In this paper, we propose Malope, an efficient memory-aware and locality-preserved GNN training framework designed for heterogeneous memory systems. First, Malope introduces a memory-aware graph partitioning strategy that preserves multi-hop connectivity and maximizes fast memory utilization. Second, Malope presents a novel locality-preserved GNN training mechanism that reorganizes mini-batches to enhance data locality, thereby improving fast memory hit rates and minimizing partition switching overhead. Additionally, Malope integrates pipelined GNN training and partition switching to minimize data transfer overhead under low bandwidth conditions. Lastly, Malope enables fine-grained model persistence, built on reorganized mini-batch training, for rapid failure recovery. Experimental results on large real-world datasets show that Malope significantly outperforms state-of-the-art GNN training frameworks, achieving an impressive average speedup of <inline-formula><tex-math notation="LaTeX">$1.51\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>51</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="cheng-ieq1-3705364.gif"/></alternatives></inline-formula>.

A spectral heterogeneous graph neural network with multi-sensor fusion for machine fault diagnosis

VulGNN: A high-fidelity graph neural network framework for robust smart contract vulnerability detection

EdgeSAC: Graph Neural Soft Actor-Critic for Hierarchical IoV Resource Management

Intelligent Transportation Systems (ITS) rely on the Internet of Vehicles (IoV) to sustain high data rates and low latency under dynamic and heterogeneous conditions. Joint power and spectrum control across macro and micro tiers remains challenging due to mobility, interference coupling, and large continuous action spaces. EdgeSAC is a graph-aware Soft Actor Critic (SAC) framework executed at the edge for power control in hierarchical Fifth-Generation New Radio (5G NR) Multiple-Input Multiple-Output (MIMO) networks. A permutation-equivariant Graph Neural Network (GNN) with edge updates encodes co-channel interference among Base Stations (BSs) and outputs node-level power fractions under tier budgets. An on-demand scheduler activates fixed-size channels and assigns at most one macro and one micro resource per user to realize dual connectivity. Signal-to-Interference-plus-Noise Ratio (SINR) is mapped to rate using a Shannon with gap model with rank adaptive MIMO, enabling tier aggregation without action discretization. In simulation with Third Generation Partnership Project (3GPP) TR 38.901 path loss and Manhattan mobility, EdgeSAC increases throughput over SAC and Proximal Policy Optimization (PPO) and reduces power relative to Twin Delayed Deep Deterministic Policy Gradient (TD3), which raises energy efficiency and fairness. The findings indicate that interference-aware graph embeddings combined with entropy regularized continuous control provide a scalable and power-efficient solution for hierarchical IoV resource management.

Sustainable energy management in IoT-enabled hybrid microgrids via graph neural network-optimized automatic generation control

Automated generation and surrogate modeling of 3D urban wind simulation based on physics-informed graph neural networks: a two-stage framework

Construction supply-chain carbon footprint with graph neural network-based input-output framework

Information bottleneck-based correlation analysis with graph neural ODEs for network reconstruction