graph neural networks

Digital twin-informed spatiotemporal graph neural network for buffeting response prediction of a long-span bridge

A machine learning framework for enhancing threat detection in core banking systems

Abstract One of the biggest issues around using machine learning (ML) for cybersecurity in commercial banking is the gap between proof-of-concept and in production. Not every model that has a high accuracy score on a benchmark dataset is the same model as one that is operating in a live transaction pipeline, subject to...

Effects of relational graph modularity and depth on the learning performance of neural networks

In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention. While some recent studies have started to explore the relationship between the graph structure of neural networks and their predictive performance, they often limit th...

Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices as graphs. Covariance matrices are ubiquitous across domains, and hence, the deployment of GNNs often leverages graphs of pairwise statistic...

ProMeta: Few-shot PROTAC-targeted degradation prediction across E3 ligases

ProMeta, a prototype-based graph neural network trained through episodic meta-learning on source-E3 tasks and evaluated on held-out target-E3 tasks through support-conditioned inference, is presented as a practical framework for cross-ligase few-shot prediction under the evaluated support/query protocols.

Topology transferable physics-informed graph learning for national-scale AC optimal power flow

Digital Reconstruction and Cross-Cultural Knowledge Service of Lingnan Garden Heritage

In this study, the author focused on the digital preservation and cross-cultural communication of Guangzhou Thirteen-Line Garden, a typical heritage site of the Maritime Silk Road. A dual-cycle Building Information Modeling-Convolutional Neural Network fusion framework was constructed to maintain dynamic consistency be...

Automatic segmentation of skeleton drainage pattern using local basin graph based on graph neural network

Traffic prediction via spatial-temporal graph learning network based on multi-domain temporal gated self-attention and attentive spectral graph attention network

Recently, traffic prediction, which serves as a fundamental component of intelligent transportation systems, has become essential for traffic planning and management. Nevertheless, existing traffic prediction methods still face challenges in capturing critical information from traffic data, such as spatial-temporal dep...

Path optimization for balanced allocation of educational resources based on graph neural networks

This study provides a novel datadriven paradigm for the equitable allocation and dynamic flow of resources within the framework of smart education by proposing an End-to-End Balanced Scheduling Model Based on Heterogeneous Graph Attention Encoding and Proximal Policy Decoding.

Deterministic Topological Retrieval-Augmented Generation via Discrete Dynamical Basins

Retrieval-Augmented Generation (RAG) systems navigate a fundamental trade-off between the computational simplicity of flat vector indices and the structural richness of graph representations. Standard dense retrieval collapses document discourse into isolated embeddings, causing semantic fragmentation and context loss....

Integrating Low-Cost xTB Electronic Priors for Predicting Electronic and Excited-State Properties of Organic Conjugated Molecules.

Accurate prediction of electronic and excited-state properties is important for the computational screening of organic conjugated molecules. However, this task remains challenging because these properties depend on both local chemical environments and global structural and electronic information. In this work, we devel...