graph neural networks

Deep Learning for Polymer Informatics: A Critical Analysis of Representations, Architectures, and Evaluation Practices

It is argued that realizing deep learning’s full potential requires not only architectural innovation but also domain-aware representations that encode the statistical ensemble nature of polymers, evaluation protocols aligned with discovery scenarios, and physically grounded inductive biases.

Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging

An enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.

A literature review of recurrent neural network approaches to malaria outbreak prediction in Sub-Saharan Africa

The review points out four persistent gaps: the lack of attention-augmented RNNs for malaria forecasting in Southern Africa, limited integration of health-facility infrastructure features with climate predictors, inadequate handling of missing data in African satellite-derived climate series, and the absence of operational dashboards that make model outputs usable for district health officers without statistical training.

Scene-Adaptive Task Offloading in Heterogeneous Edge Networks via Graph Neural Network-Enhanced Deep Reinforcement Learning

SAGE (Scene-Adaptive Graph-Enhanced offloading), a task-offloading framework that combines a heterogeneous graph neural network (HeteroGNN) with a dueling double DQN meta-controller and a mixed-integer linear programming (MILP) solver, is proposed, demonstrating strong scene-adaptive decision-making capability.

Dynamic routing and wavelength assignment in optical networks: from capacity limits to flow-based control

Graph Neural Networks for Diffusion and Aggregation in Wireless Federated Learning

User devices (UDs) with non-independent and identically distributed (non-IID) data will worsen accuracy performance of the global model in federated learning (FL). Therefore, the implementation of diffusion strategies in machine learning (ML) models can enhance the effectiveness of federated learning with non-IID data. However, in a device-to-device (D2D) wireless federated learning (WFL) system, limited wireless resources and severe wireless channel interference become the important bottleneck to restrict the diffusion performance and model aggregation so as the global model of WFL with non-IID suffers from the weight divergence challenge. Thus, we propose a novel joint over-the-air computation (OAC) aggregation and diffusion framework by using a graph neural network (GNN) for WFL, termed an OAC-GNN-Dif framework. By integrating the OAC with message passing neural network (MPNN) of GNN, we further develop the OAC-MPNN-Dif algorithm based on the OAC-GNN-Dif framework. To further reduce communication costs, we designed an OAC message recurrent neural network (OAC-MPRNN-Dif) algorithm, where each UD propagates local models via D2D communications to refresh the graph embedding in the current frame based on the graph feature extraction and localization state of the previous frame to reduce communication costs. Additionally, we introduce dynamic time-varying MPNN for federated diffusion within evolving D2D network topologies. The experimental results indicate that our approach significantly performs well in communication overhead, with a 30%-60% decreasing in wireless resources overhead and 1.2-3.5 times decreasing in the number of model transfers compared to the FedDif methods. Moreover, our approach also improves the global model test accuracy, which is about 2.7% higher than the existing communication diffusion FL with non-IID characteristics.

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