graph neural networks

Simplicial Graph-Based Detection and Localization of Cyber Attacks Against Large-Scale Smart Grids

Ensuring the resilience of large-scale power systems against cyber attacks is critical to maintaining the stability of modern cyber-physical energy systems. Existing machine learning (ML)-based detection frameworks predominantly focus on pairwise node interactions (i.e., edges) and often overlook capturing higher-order...

HVAC subsystem-aligned explainable graph neural network mixture of experts (GNN-MoE) for multi-horizon work order prediction

Dynamic response analysis method of parametric structures based on modal reduction basis predicted by physics-informed graph neural networks

Full-Reference Point Cloud Quality Assessment Using Graph Neural Network-Based Regression

Edge-Girth as a Structural Edge Feature for Graph Neural Networks

On graph discrimination it is proved a matching limitation: on graphs where every edge sees the same number of shortest cycles of the same length, the descriptor becomes constant and any model built on it collapses back to the 1-WL bound.

Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing

Inspired by graph-based manifold learning, a manifold-aware encoding strategy for general coded computing (GCC) is proposed and experiments demonstrate that the proposed strategy consistently and significantly reduces the mean squared recovery error under straggling compared with standard GCC.

Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials

It is proved that the Hypergraph Neural Network, an invariant architecture with 3-body message passing, is a universal approximator for potential energy surfaces and provides the first rigorous justification for the common practice of using multi-layer message passing with a per-layer cutoff smaller than the physical i...

Automated Vulnerability Patching in Legacy Code Using LLMs and Multi-AI Agents

While multi-agent LLM pipelines show real promise for legacy code specifically, since their capacity for autonomous context reconstruction and iterative, feedback-driven refinement directly addresses the sparse documentation and thin test coverage that define legacy environments, the literature to date has been validat...

A Systematic Literature Review on Software Testing Prediction Models

The findings show a shift from traditional statistical models to advanced artificial intelligence techniques, including graph neural networks, contrastive learning, and deep fuzzy clustering, while explainable artificial intelligence enhances model interpretability.

AI-Powered Anomaly Detection in Cloud-Based Applications

The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.

Hierarchy-Aware Semantic Losses for Knowledge Graph Link Prediction

This paper combines graph neural network encoders with box-embedding-based semantic losses that encourage learned representations to better satisfy ontology-derived class hierarchies, and compares this approach to both standard link prediction models and models incorporating subclass relations as graph edges.

Energy Consumption Graph Inference for Financing Risk Prediction in Industrial Enterprises

Energy cost financing is increasingly used by industrial enterprises to manage electricity, gas, steam, and fuel expenses during periods of high production demand or cash-flow pressure. However, abnormal energy consumption, declining production intensity, delayed utility payments, and unstable downstream orders may sig...