graph neural networks

HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs

HyGRAIL, a cost-aware and evidence-grounded framework that combines heterogeneous GNN triage with LLM-based hypothesis review, is proposed, which retrieves node-level associations and multi-hop relational paths from the knowledge graph and converts this structured evidence into natural language through template-based o...

H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression

H3DNAS is presented, a hardware-aware model compression framework that operates directly on ONNX computational graphs without requiring original source code, architecture class definition, or gradient access during search during search, and is the first source-code-free compression pipeline for 3D point cloud models.

Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

This work proposes a method for refining heuristic-obtained clusters by grounding the authors' clustering on contrastive embeddings yielded by graph neural networks, and releases a publicly available dataset of Bitcoin transaction graphs containing a substantial number of clusters.

Quantum-Enhanced Graph Neural Networks

Quantum-Enhanced Graph Neural Networks (QEN) presents a novel approach to graph neural network (GNN) learning, leveraging the principles of quantum entanglement to enhance both the training process and the network's expressive capabilities. This research investigates a method for utilizing entanglement to accelerate tr...

Life Operators: A Self-Evolving Framework for Multiscale Life Modelling

Medical AI is moving beyond recognition towards clinical dialogue and longitudinal prediction. Yet a central question remains: how would a patient’s state change under intervention? Statistical models learn future observations, whereas mechanistic models describe selected processes. Neither provides a common framework...

Messages Passed Along the Edges: A Contemporary Synthesis Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive...

Graph Neural Network-Based Program Vulnerability Prediction

This paper investigates the application of Graph Neural Networks (GNNs) for program vulnerability prediction. Traditional methods for vulnerability detection often rely on static analysis and signature-based approaches, which can be limited in their ability to capture complex code relationships and identify novel vulne...

Adaptive Topological Optimization via Graph Neural Networks

This paper introduces a novel optimization framework based on graph neural networks (GNNs) designed for geometric transformation optimization. Traditional optimization methods often rely on handcrafted objective functions and are limited in their ability to handle complex geometric transformations. This work leverages...

Intelligent scheduling of aerospace precision forgings based on deep reinforcement learning and graph neural networks

To address the issues of extreme time-consuming bottlenecks such as special heat treatments in the manufacturing of aerospace precision forgings and scheduling failures caused by sudden rush orders, an intelligent scheduling model based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) is proposed. T...

Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces.

This study developed and evaluated three different UQ frameworks based on advanced UQ methods and integrated with graph neural networks: Bayesian Neural Networks, Evidence Neural Networks, and Monte Carlo Dropout, and shows that ENN effectively reflects data adequacy, while MCD exhibits excellent robustness.

Neural Network-Based Accelerated Model for Condensed Combustion Products in Solid Propellant

Graph Neural Network and Artificial Based Models in Preclinical Pharmacology: Predictivity, Opportunities and Challenges

Major challenges in preclinical pharmacology, such as low predictivity, high rates of attrition, prolonged development cycles, and high costs remain significant issues as they largely relate to how translational relevance of past experimental models. This work aims to assess the potential of artificial intelligence-bas...