graph neural networks

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...

Default Contagion Detection in Industrial Enterprise Networks Using Multi-Relational Graph Neural Inference

The results demonstrate that multi-relational graph neural inference can capture default transmission mechanisms that are difficult to observe through firm-level indicators alone.

A compact physics-informed graph neural network for real-time wind-pressure prediction of tensioned membrane roofs across multiple wind directions

Designing a Good Virtual Node: Addressable and Cardinality-Preserving Global Memory for Message Passing Architectures

Experiments on multiplicity-aware Two-Radius, motif counting, and constrained link-set prediction support this addressable and cardinality-preserving virtual memory at (O(nMd)) arithmetic cost.

Leveraging Cyber-Physical Security Solutions Blended With Machine Learning for Advanced IoT Botnet Detection

The proliferation of Internet of Things (IoT) ecosystems has significantly increased the attack surface of cyber-physical systems, leading to the emergence of large-scale botnets that exploit device vulnerabilities for distributed and persistent attacks. This survey comprehensively reviews state-of-the-art techniques t...

LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.

This work proposes LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions, and demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitat...

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction

PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...