graph neural networks

Navigating committor landscape of biomolecules with a general pairwise interaction model

A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.

CoRe-GNN: Multilevel Message passing on Coarsened graphs

CoRe-GNN is proposed, which performs both propagations in parallel at each layer: a coarsened inter-cluster term capturing long-range structure, and a local intra-cluster term preserving per-node discriminability.

Physics informed inverse discovery of potential topological materials via descriptor-graph ensemble learning

This work establishes a scalable and physics guided candidate prioritization framework for inverse discovery of TMs beyond brute force electronic structure calculations and topological studies.

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 i...

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 operati...

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.

Контроль достоверности многоканальной телеметрии робототехнического манипулятора на основе графа межканальной согласованности

Работа посвящена контролю качества данных в многоканальной телеметрии робототехнического манипулятора. Цель исследования – разработать и экспериментально оценить метод, который выявляет и локализует искаженный канал без обучения нейронной сети и без физического стенда. Метод объединяет показатели полноты, временной кор...

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....

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)