graph neural networks

A Dynamic Graph Neural Network Framework for Advanced Persistent Threat Detection in Cloud Computing

Although the framework performed slightly better than the best baseline under the tested conditions, the improvement was not statistically significant enough to establish consistent superiority and emphasize the need for further evaluation of deployment time, base-rate estimation, and real-world cybersecurity scenarios...

Redefining tropical photovoltaics: an AI–DFT-driven physics-constrained framework linking electronic structure, climate response, and device-level performance in CsPbI3 perovskites

Overall, the proposed AI–DFT framework provides a physically interpretable computational approach for climate-aware prioritization of perovskite photovoltaic materials by connecting quantum-derived descriptors, ML predictions, environmental factors, sensitivity-based robustness assessment, and device-level performance...

Hidden Axis of Uncertainty: Latent-Posterior Alignment in Graph Neural Networks with Bayesian Output Layers

This work observes that predictive uncertainty decreases as latent representations shift toward lower-variance posterior directions, even though the posterior variance does not contract, and proposes Alignment-Guided Learning (AGL), which explicitly promotes this alignment during training.

Interference-Calibrated Algebraically Projected Antenna Selection with Certified Graph Learning for Massive MIMO Under Realistic Multi-Cell Impairments

Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation...

ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning

ReCoG (Reciprocal Co-Evolution for Multimodal Graph Learning) is proposed, a new learning paradigm that tightly couples graph structure learning and multimodal representation learning through end-to-end reciprocal interaction and yields greater expressiveness than decoupled or two-stage formulations.

Multi-Agent Reinforcement Learning for Dynamic Inventory Rebalancing and Last-Mile Fulfillment Under Supply Chain Disruptions

Experiments show that prediction must be coupled with autonomous optimization to deliver practical resilience, and show that the learned policy sustains service levels, shortens recovery time, and reduces total disruption cost relative to base-stock and single-agent baselines.

Imitation Learning for Connection-Tableau Construction

An automated theorem prover builds a proof step by step, choosing at each point what to add and what to remove, and learns policies that solve up to 46% more problems than leanCoP, and reach proofs in an order of magnitude fewer steps.

Feasibility Study on Inferring Construction Workers’ Roles and Cooperation Status Using Graph Neural Networks

CrystalGRW: generative modeling of crystal structures with targeted crystallographic properties via geodesic random walks

CrystalGRW is introduced, a diffusion-based generative model on Riemannian manifolds that proposes candidate crystal configurations in stable phases, validated through density functional theory calculations, thereby accelerating materials discovery and inverse design.

基于多模态信息融合的神经符号推理引擎

This paper introduces a novel neuro-symbolic reasoning engine designed to tackle complex problems by integrating the strengths of deep learning and symbolic reasoning. The core concept is to fuse multi-modal information – including images, text, and audio – to create a richer understanding of the problem domain. This u...

Enhanced air quality prediction using probabilistic spiking neural networks with multi-aspect graph attention mechanism

基于自适应的神经网络的图论优化

This paper presents a novel graph theory-based optimization algorithm for neural network architecture design, leveraging the inherent properties of graph structures to enhance network performance. Traditional neural network architectures often rely on fixed configurations, while this approach dynamically adjusts networ...