graph neural networks

Geometric and topological deep learning for predicting thermo-mechanical performance in cold spray deposition process modeling

This study presents a geometric deep learning framework for predicting cold spray particle impact responses using finite element simulation data. A parametric dataset was generated through automated Abaqus simulations spanning a systematic range of particle velocity, particle temperature, and friction coefficient, yiel...

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

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 the mu...

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...

Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs

Results indicate that peculiar velocities provide the dominant source of $\Omega_m$ information for set-based models in this setting, while spatial information is most effectively used by architectures that explicitly encode galaxy-galaxy relations.

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

Experimental results demonstrated that the integrated framework significantly improves geometric-texture alignment, reduces system rendering delay, and effectively extends user engagement and learning experience.

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

A novel spatial-temporal graph learning network (ST-GLN) based on multi-domain temporal gated self-attention and attentive spectral graph attention network is proposed in this paper, which can efficiently capturing crucial information from traffic data.

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.