graph neural networks

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...

Multi-Modal Knowledge Graph Reasoning Engine

This paper introduces a novel Multi-Modal Knowledge Graph Reasoning Engine designed to facilitate complex reasoning and knowledge discovery through the interactive integration of diverse knowledge graph modalities. The core claim is that leveraging the interplay between multi-modal knowledge graphs significantly enhanc...

基于神经符号人工智能的知识推理

This paper explores a novel approach to artificial intelligence by integrating the strengths of neural networks and symbolic reasoning. The core idea is to leverage the pattern recognition capabilities of neural networks for knowledge extraction and representation, coupled with the logical deduction and inference capab...

Dynamic Topology Neural Networks (DTNNs) Learning

This paper introduces Dynamic Topology Neural Networks (DTNNs), a novel approach to neural network design that leverages continuous topological evolution and feedback learning to autonomously construct and optimize network topologies. Unlike traditional static neural networks, DTNNs adapt their structure in real-time,...

Temporal Graph Neural Networks with Adaptive Relational Strength

This paper introduces a novel approach to Graph Neural Networks (GNNs) designed to effectively capture temporal dependencies within dynamic graph structures. Traditional GNNs often fail to adequately represent the evolving nature of relationships between nodes, hindering their performance in systems where the graph top...

RGDLAN and HMARN: A dual-path graph neural approach with multi-agent systems for listed enterprise violation prediction

Title: Non-Local Graph Neural Networks for Semantic Representation

This paper explores the application of non-local graph neural networks to enhance semantic representation learning. Traditional graph neural networks primarily focus on node features, often struggling to capture the nuanced relationships within a graph. We propose a novel architecture that explicitly models the *meanin...